US20150153376A1 - Method and apparatus for the alignment of multi-aperture systems - Google Patents
Method and apparatus for the alignment of multi-aperture systems Download PDFInfo
- Publication number
- US20150153376A1 US20150153376A1 US14/617,815 US201514617815A US2015153376A1 US 20150153376 A1 US20150153376 A1 US 20150153376A1 US 201514617815 A US201514617815 A US 201514617815A US 2015153376 A1 US2015153376 A1 US 2015153376A1
- Authority
- US
- United States
- Prior art keywords
- vehicle
- accelerometer
- memory
- data set
- sensor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
- G01P15/02—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration by making use of inertia forces using solid seismic masses
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
- B60W40/11—Pitch movement
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
- B60W40/112—Roll movement
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
- B60W40/114—Yaw movement
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/04—Monitoring the functioning of the control system
- B60W50/045—Monitoring control system parameters
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0002—Automatic control, details of type of controller or control system architecture
- B60W2050/0013—Optimal controllers
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0062—Adapting control system settings
- B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
- B60W2050/0083—Setting, resetting, calibration
- B60W2050/0085—Setting or resetting initial positions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
- B60W40/107—Longitudinal acceleration
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
- B60W40/109—Lateral acceleration
Definitions
- Next generation automotive systems such as Lane Departure Warning (LDW), Collision Avoidance (CA), Blind Spot Detection (BSD) or Adaptive Cruise Control (ACC) systems will require target information from multiple sensors including a new class of sensor called sensor apertures such as radar, image or laser, similar to those found on advanced tactical fighter aircraft.
- sensor apertures such as radar, image or laser
- one sensor aperture may be located on the front bumper of the vehicle and obtains range and azimuth information about vehicles and stationary objects in front of the vehicle.
- Another sensor aperture may be located on the dash of the vehicle and obtains image information about vehicles and stationary objects in front of the vehicle.
- Another sensor aperture may be located on the side of the vehicle and obtains either range and azimuth data or image data in order to determine velocity and track information on vehicles that pass the vehicle.
- SA Situation Awareness
- SAP Situation Awareness Platform
- SF Sensor Fusion
- NF Navigation Fusion
- One method of aligning the sensors apertures to each other and to the vehicle is to use mechanical and optical instruments, such as auto-collimators and laser boresight tools, during the production of the vehicle.
- This technique is not only costly, but would be require if a sensor aperture were repaired or replaced after production. An alignment procedure would have to be performed again in order to assure the safety critical systems were reporting accurately. Also as the vehicle goes through normal wear and tear the sensor apertures would start to become misaligned and may not be noticed by the operator. This means that the data from the sensor apertures would not correlate with each other and the vehicle reference frame until the sensor apertures were aligned again. Again, this would be costly to the vehicle operator and until performed, the SAP may not provide accurate data. Therefore, a method to align the sensor apertures to each other and to the vehicle without the use of sophisticated optical tools is required. This patent addresses this problem by describing methods that can be used to align the sensor apertures to each other and to the vehicle that do not require external alignment equipment.
- U.S. Pat. No. 5,245,909 Automatic Sensor Alignment
- U.S. Pat. No. 5,245,909 relates to systems for maintaining alignment-sensitive aircraft-borne avionics and weapons sensors in precise alignment. It further relates to methods for precisely aligning sensitive avionics for weapons system instrumentation, which is subject to vibrations causing misalignment.
- this disclosure relates to methods and systems that support advanced automotive systems not described in the prior art.
- a second key difference is the reliance of sensor data from the vehicle as part of the alignment method. Another difference is using image apertures with elements of the vehicle in the field of view of the imager and employing optical methods for determining changes to the alignment with respect to the vehicle and vehicle reference frame, then applying a compensation based on the misalignment angle measured.
- this system described herein does not require a reliance on boresighting and aligning any sensor to achieve a vehicle reference frame.
- U.S. Pat. No. 6,202,027 Automatic Curve Sensor Calibration
- U.S. Pat. No. 6,202,027 Automatic Curve Sensor Calibration
- Different sensors can be used in vehicles to identify objects and possible collision conditions.
- an optical sensor such as a camera
- Another Infrared (IR) sensor may be mounted in the front grill of the vehicle.
- a third inertial sensor may be located in yet another location in the central portion of the vehicle. Data from these different sensors is correlated together to identify and track objects that may come within a certain vicinity of the vehicle.
- the measurements from the different sensors must be translated to a common reference point before the different data can be accurately correlated. This translation is difficult because the sensors are positioned in different locations on the vehicle. For example, the sensor located inside the front bumper of the vehicle may move in one direction during a collision while the sensor located on the top of the vehicle roof may move in a different direction.
- One of the sensors may also experience vibrations at a different time than the other sensor.
- the front bumper sensor may experience a vertical or horizontal movement when the vehicle runs over an obstacle before any movements or vibrations are experienced by the roof sensor. This different movements of sensors relative to each other make is very difficult to accurately determine the precise position and orientation of the sensors when the sensor readings are taken. This makes it difficult to translate the data into common reference coordinates.
- the present invention addresses this and other problems associated with the prior art.
- a vehicle sensor system configured to gather sensory data 360 degrees around the vehicle, comprising of sensor apertures for gathering data such as: range (e.g. ultrasonic); range and azimuth (e.g. laser and/or radar); images (e.g. optical and/or thermal).
- the vehicle has sensors that align and establish a vehicle reference frame by measuring body yaw, pitch and roll rates as well as acceleration along the 3 axes of the vehicle.
- the imaging apertures that have a clear view of body mold lines, like hood or rear deck, will align themselves to the vehicle reference frame, those apertures that can not align using optical methods are aligned to the vehicle using accelerometers and rates sensors by reading the inertial acceleration or angular rotation to align themselves to each other.
- An Integrated Computing Platform hosts the SAP software that maintains complete system alignment by determining differences in alignment and applying or updating a compensation value with respect to the vehicle body coordinates resulting in a dynamically boresighted system.
- a multi-sensor system includes multiple sensors that are integrated onto the same substrate forming a unitary multi-sensor platform that provides a known consistent physical relationship between the multiple sensors.
- a processor can also be integrated onto the substrate so that data from the multiple sensors can be processed locally by the multi-sensor system.
- FIG. 1 is a diagram showing how a common inertial acceleration is sensed by accelerometers on each sensor and can be used to align the sensor coordinate frames.
- FIG. 2 is a diagram showing the pitch angles used to determine the pitch misalignment angle of the optical sensor.
- FIG. 3 is a diagram showing the yaw data that is used to determine the yaw misalignment angle of the optical sensor.
- FIG. 4 is a diagram showing the roll data that is used to determine the roll misalignment angle of the optical sensor.
- FIG. 5 is an image showing the top of the hood and how it is used to compute the pitch misalignment angle.
- FIG. 6 is a magnified image of the hood line showing the pixels of the image.
- FIG. 7 is an image showing the top of the hood and how it is used to compute the roll misalignment angle.
- FIG. 8 is a magnified image of the banked hood line showing the pixels of the image.
- FIG. 9 is an image showing the top of the hood and how it is used to compute the yaw misalignment angle.
- FIG. 10 is a flow chart that shows the alignment process when all sensors have micro-inertials.
- FIG. 11 is a flow chart that shows the alignment process when using micro-inertials and an optical sensor.
- FIG. 12 is a flow chart that shows the alignment process when all of the sensors are optical.
- FIG. 13 is a flow chart that shows the alignment when the sensors are on a common platform.
- FIG. 14 is a block diagram of a multi-sensor system.
- FIG. 15 is a block diagram of an alternate embodiment of the multi-sensor system that includes an on-board processor.
- FIG. 16 is a flow diagram showing how the processor in FIG. 15 operates.
- FIG. 17 is detailed diagram showing how different elements in the multi-sensor system are electrically connected together.
- FIG. 18 is a diagram showing how different multi-sensor systems operate together to track objects.
- One method is to attach three axis accelerometers to each sensor and to the vehicle and use gravity and the acceleration of the vehicle, which will be sensed by the accelerometers, to align the sensor axes to each other and to the vehicle.
- Information from the vehicle that is available on the Car Area Network (CAN) bus will also be used to perform the calculation of the misalignment angles.
- FIG. 1 shows in two dimensions the relation between sensor aperture A frame, sensor aperture B frame and the vehicle body reference frame.
- the vehicle experiences a linear acceleration and this common acceleration is observed by the accelerometers located on sensor aperture A, sensor aperture B and the vehicle body.
- the accelerometers that are attached to the vehicle body are aligned to the vehicle body reference frame.
- the misalignment angle between the two sensor apertures .theta.sa-.theta.sb, can be computed.
- the same can be done between sensor aperture A and the vehicle body, and sensor aperture B and the vehicle body to compute all of the misalignment angles.
- This approach can be used to compute the three dimensional misalignment angles of roll, pitch and yaw between sensor apertures and the vehicle body reference frame.
- Acomp Asensora ⁇ wxwxR1
- Acomp Asensora ⁇ wxwxR1
- Asensora the acceleration measured by sensor A accelerometers
- Asensorb the acceleration measured by sensor B accelerometers
- w the angular rotation of the vehicle measured by the ref gyros x is the cross product of two vectors
- R1 is the lever arm vector between sensor A and sensor B
- Acomp is the sensor acceleration compensated for lever arm rotation.
- the accelerometer groups will sense gravity and this can be used to help compute some of the misalignment angles.
- Information from the vehicle CAN bus, such as wheel rotation speeds are zero, will tell the Kalman filter that the vehicle is not moving and the only sensed acceleration will be from gravity.
- FIG. 10 is a flow chart showing the process when all of the sensor apertures, as well as the vehicle body, have a micro-inertial attached to them.
- the micro-inertials sense the angular rotation and/or acceleration of the vehicle and this information is the input to a Kalman filter.
- the filter uses this information to estimate the roll, pitch and yaw misalignment angles between a sensor aperture and the vehicle body frame. These misalignment angles are then used to rotate the sensor target data into the vehicle body frame. With all of the target data in a common reference frame the processor can fuse data from several sensors into an optimal target track file.
- the second method is to use accelerometers to align the sensor apertures to each other and one of the sensor apertures is aligned to the vehicle body by using optical information from the sensor aperture itself.
- acceleration data can be used to align sensor aperture A to sensor aperture B, but sensor aperture B is aligned to the vehicle body directly by using sensor aperture B to compute the misalignment angles between sensor aperture B and the vehicle body. Since sensor aperture A is aligned to sensor aperture B and sensor aperture B is aligned to the vehicle body, you can compute the misalignment between sensor aperture A and the vehicle body.
- Sensor aperture B can be a visual sensor aperture, such as a video camera, and by observing the outline of the hood and body of the vehicle using this camera, you can compute the misalignment angles between sensor aperture B and the vehicle body frame.
- FIG. 2 shows that the pitch misalignment angle is the angle between the sensor aperture's X-axis and vehicle's X-axis in the vertical plane.
- the pitch angle between the vehicle X-axis and a line from the sensor aperture to the top point of the hood, .PHI.vehicle can be computed from the vehicle's dimensions.
- the image from the sensor aperture FIG. 5 for example, shows the top of the hood.
- Pp By counting the pixels from the center of the image down to the hood, Pp, the sensor aperture pitch angle can be computed. Using a 480.times.640 pixel image, this angle can be computed to within 1 pixel, see FIG. 6 .
- FIG. 3 shows that the small yaw misalignment angle is the angle between the sensor aperture's X axis and vehicle's X axis in the horizontal plane.
- the sensor aperture image shows the left and right edges of the hood, FIG. 9 .
- FIG. 4 shows that the small roll misalignment angle is the angle between the sensor aperture's Y-axis and vehicle's Y-axis in the vertical plane.
- the sensor aperture image shows that the hood line and the sensor aperture level lines cross to form the roll misalignment angle. This is shown in FIG. 7 .
- FIG. 8 shows that the hood line can be determined accurately to within a couple of pixels.
- FIG. 11 is a flow chart showing the process when at least one of the sensors is an optical device. All of the sensors have a micro-inertial attached to them. The optical device can see the targets and the outline of features of the vehicle, such as the hood line. The optical sensor uses the hood line information to compute the roll, pitch and yaw misalignment angles between the optical sensor frame and the vehicle body frame.
- the micro-inertials sense the angular rotation and/or acceleration of the vehicle.
- the Kalman filter estimates the roll, pitch and yaw misalignment angles between a sensor aperture frame and the optical sensor frame. These misalignment angles as well as the misalignment angles between the optical sensor and the vehicle body frame are then used to rotate all of the sensor target data into the vehicle body frame. Again, with all of the target data in a common reference frame the processor can fuse data from several sensors into an optimal target track file.
- a third method is to use optical information from sensor aperture A and sensor aperture B to compute the misalignment between the two sensor apertures and to use optical information from sensor aperture B to compute the misalignment between sensor aperture B and the vehicle body.
- sensor aperture A can be a ranging laser sensor aperture and it sends out multiply beams of light to detect a target.
- sensor aperture B can also detect the reflected light in its video camera and using this information it can compute the misalignment between sensor aperture A and sensor aperture B.
- FIG. 12 is a flow chart showing the process when all of the sensors on the vehicle are optical sensors.
- Each optical device can see targets and the outline of features of the vehicle, such as the hood or truck line.
- the optical sensors use this vehicle body information to compute the roll, pitch and yaw misalignment angles between the optical sensor frame and the vehicle body frame. These misalignment angles are then used to rotate the sensor target data from each sensor into the vehicle body frame.
- the processor can fuse data from several sensors into an optimal target track file.
- a fourth method is to collocate all of the sensor apertures into one box that is mounted on the vehicle, such as the roof, so that all sensor apertures are always aligned with respect to each other and the only alignment required is the alignment between this sensor aperture box and the vehicle body. This can be performed by using a set of accelerometers in the sensor aperture box and on the vehicle body frame or optically by using a video camera in the sensor aperture box.
- FIG. 13 shows the case where all of the sensors are mounted onto one fixed platform. If one of the sensors is an optical sensor then it can be used to align the platform frame to the vehicle body frame as shown above. Once this set of misalignment angles is computed, then all of the target data from all of the sensors can be rotated to the common vehicle body reference frame. As shown above all of the target data is now in one reference frame for computing the optimal target tracks. If none of the sensors are optical, then a set of micro-inertials can be mounted on the common platform and also on the vehicle body. While the vehicle is moving the Kalman filter can now be used to compute the misalignment angles as discussed in the above paragraphs.
- the systems described above can use dedicated processor systems, micro controllers, programmable logic devices, or microprocessors that perform some or all of the operations. Some of the operations described above may be implemented in software and other operations may be implemented in hardware.
- FIG. 14 shows a multi-sensor system 20812 that includes different sensors 20816 and 20818 that are both integrally attached to or integrally formed into the substrate 20814 . Because the two sensors 20816 and 20818 are integrated onto the same substrate 20814 , any forces experienced by sensor 20816 are also experienced by sensor 20818 .
- One type of material that is used for substrate 20814 is invar. Invar is a rigid metal that has been cured with respect to temperature so that its dimensions do not change with fluxuations in temperature. Any rigid material that is resilient to expansion or contraction with temperature changes can be used.
- Locating the sensors 20816 and 20818 on the same substrate 20814 simplifies the cost of sensor manufacturing and installation.
- the two sensors 20816 can be assembled onto the substrate 20814 in a factory prior to being installed on a vehicle. If the two sensors 20816 and 20818 were not mounted on the same substrate 20814 , then each sensor would have to be separately mounted on the vehicle and then calibrated to a known alignment with respect to each other. Even if the two sensors were installed correctly, changes in the shape of the vehicle due to wear, temperature, etc. over time could change the initial alignment between the two sensors.
- Premounting or prefabricating the sensors 20816 and 20818 on the substrate 20814 prior to installation on a vehicle prevents these alignment errors. Only the substrate 208 14 of the multi-sensor system 20812 has to be mounted to the vehicle, not the individual sensors 20816 and 20818 . This allows the relative position 20820 and alignment between the two sensors 20816 and 20818 to remain the same regardless of how the substrate 20814 is mounted on the vehicle.
- Wiring is also simplified since only one wiring harness has to be run through the vehicle to the multi-sensor system 20812 .
- the senor 20816 senses an area 20824 and the sensor 20818 senses an area 20822 that are both coincident.
- One of the sensors may have a wider field of view than the other sensor.
- Some examples of sensors include ultrasonic, Infra-Red (IR), video, radar, and lidar sensors.
- the sensors may be separate components that are glued or bolted onto the substrate 20814 . If the multi-sensor system 20812 is an integrated circuit, then the sensors 20816 and 20818 may be integrally fabricated onto a silicon or alternative temperature resilent substrate 20814 using known deposition processes.
- the radar sensor 20814 may only be able to measure angle of an object to within one-half a degree. Because of the limited angle accuracy of the radar angle readings, it may not be possible to determine from the radar reading along if an oncoming vehicle is coming from the same lane of traffic or from an opposite lane of traffic.
- the video sensor 20818 may be able to accurately determine the angle of an object to within one-tenth or one-one hundredth of a degree. By correlating the radar information with the camera information, the location of an on-coming vehicle can be determined more accurately.
- the relative position and alignment between the two sensors remains essentially the same regardless of physical effects on the vehicle.
- the camera data can be correlated with radar data to within fractions of a degree of accuracy.
- a first sensor may detect one object out in front of the vehicle.
- a second sensor located somewhere else on the vehicle may detect two different objects in front of the vehicle. Because of vibrations in different parts of the vehicle, a central processor may not be able to determine which of the two objects detected by the second sensor is associated with the object detected by the first sensor. With the multi-sensor system 20812 , measurement errors caused by this vehicle vibration is cancelled since the two sensors 20816 and 20818 effectively experience the same amount of vibration at the same time.
- FIG. 14 shows an alternative embodiment where a processor 20826 is mounted to the substrate 20814 .
- the processor 20826 can be a standalone component that is rigidly attached to substrate 20814 .
- the processor 20826 is a portion of the same integrated circuit that also contains the circuitry for sensors 20816 and 20818 .
- the processor 20826 can perform signal processing tasks for both sensor 20818 and sensor 20816 and can also handle communication and diagnostics tasks. Tracks for identified objects are sent over connection 20828 to other multi-sensor systems in the vehicle or to a vehicle control system as shown later in FIG. 18 .
- each sensor was required to send all data back to the same central processing system. This takes additional time and circuitry to send all of the data over a bus.
- the processor 20826 By mounting the processor 20826 in the multi-sensor system 20812 , data from both sensor 20816 and sensor 20818 can be processed locally requiring fewer reports to be sent over connection 20828 .
- the processor 20826 in FIG. 14 receives radar reports from the first sensor 20816 in block 20834 .
- the processor 20826 receives image reports from the second sensor 20818 in block 20836 .
- the processor 20826 correlates the different reports in block 20838 . Since the relative position of the two sensors 20816 and 20818 are the same and possibly coincident, the processor 20826 does not have to perform as many calculations transforming sensor measurements into common body coordinates for the vehicle.
- the correlation may include first determining if the reports actually identify an object in block 20840 .
- the processor 20826 can verify or refine object detection information from one of the sensors with the message reports received from the other sensor. If both sensors do not verify detection of the same object within some degree of certainty, then the processor system 20826 may discard the message reports or continue to analyze additional reports in block 20840 .
- the processor 20826 When an object is detected in block 20840 , the processor 20826 only has to send one report in block 20842 representing the information obtained from both sensor 20816 and sensor 20818 . This reduces the total amount of data that has to be sent either to a central controller or another multi-sensor system in block 20842 .
- FIG. 17 shows in further detail the different devices that may be integrated on the multi-sensor substrate 20814 .
- Camera optics 20850 and radar transmit/receive modules 20852 are each connected to a Central Processing Unit (CPU) 20854 and a digital signal processor 20856 .
- a memory 20858 is used to store sensor data, signal processing applications and other operating system functions.
- the CPU 20854 is also used for conducting distributed sensor fusion as described in further detail below.
- different multi-sensor systems 20812 A- 20812 D are used for monitoring different zones around a vehicle 20860 .
- system 20812 A monitors zone 1
- system 20812 B monitors zone 2
- system 20812 C monitors zone 3
- system 20812 D monitors zone 4.
- the CPU 20854 and digital signal processor 20856 FIG. 17 in each multi-sensor system 20812 A- 20812 D in combination with the camera and radar sensors identify and track objects autonomously, without having to communicate with a central controller 20868 in vehicle 20860 .
- a track file is created for that object in memory 20858 ( FIG. 17 ). If the object moves to another zone around the vehicle 20860 , the multi-sensor system for the zone where the object was previously detected only has to send the track files to the other multi-sensor system associated with the overlapping region.
- a bicycle 20865 may be initially detected by multi-sensor system 20812 A at location 20864 A in zone 1.
- the multi-sensor system 20812 A creates a track file containing position, speed, acceleration, range, angle, heading, etc. for the bike 20865 .
- the bike 20865 may move into a new position 20864 B in an overlapping region 208 66 between zone 1 and zone 2.
- the multi-sensor system 20812 A upon detecting the bike 20865 in the overlapping region 20866 sends the latest track file for the bike 20865 to multi-sensor system 20812 B over bus 20862 . This allows the multi-sensor system 20812 B to start actively tracking bike 20865 using the track information received from multi-sensor system 20812 A.
- the multi-sensor system 20812 A only has to send a few of the latest track files for the common area 20866 over connection 20864 to multi-sensor 20812 B in order for system 20812 B to maintain a track on bike 208 65 .
- the track files can be exchanged between any of the multi-sensor systems 20812 A- 20812 D.
- the track file with the greatest confidence of accuracy is used for vehicle warning, security, and control operations.
- connection 20862 can a CAN bus, wireless 802.11 link or any other type of wired or wireless link.
- the system described above can use dedicated processor systems, micro controllers, programmable logic devices, or microprocessors that perform some or all of the operations. Some of the operations described above may be implemented in software and other operations may be implemented in hardware.
Abstract
Description
- This application is a continuation of U.S. patent application Ser. No. 12/698,960, filed Feb. 2, 2010, which is a continuation of U.S. patent application Ser. No. 12/024,058, filed Jan. 31, 2008, which is a continuation of U.S. Pat. No. 7,337,650, Issued Mar. 4, 2008 Titled—SYSTEM AND METHOD FOR ALIGNING SENSORS ON A VEHICLE the disclosures of which are incorporated herein by reference in their entirety and further incorporates by reference: U.S. Pat. No. 6,629,033, Issued Sep. 30, 2003 Titled—OPEN COMMUNICATION SYSTEM FOR REAL-TIME MULTIPROCESSOR APPLICATIONS, U.S. Pat. No. 6,771,208, Issued Aug. 3, 2004 Titled—MULTI SENSOR SYSTEM, and U.S. Pat. No. 7,146,260, Issued Dec. 5, 2006 Titled—METHOD AND APPARATUS FOR DYNAMIC CONFIGURATION OF MULTIPROCESSOR SYSTEM.
- Applicants believe the above-incorporated material constitutes “essential material” within the meaning of 37 CFR 1.57(c)(1)-(3), applicants have amended the specification to expressly recite the essential material that is incorporated by reference as allowed by the applicable rules.
- Next generation automotive systems such as Lane Departure Warning (LDW), Collision Avoidance (CA), Blind Spot Detection (BSD) or Adaptive Cruise Control (ACC) systems will require target information from multiple sensors including a new class of sensor called sensor apertures such as radar, image or laser, similar to those found on advanced tactical fighter aircraft. For example, one sensor aperture may be located on the front bumper of the vehicle and obtains range and azimuth information about vehicles and stationary objects in front of the vehicle. Another sensor aperture may be located on the dash of the vehicle and obtains image information about vehicles and stationary objects in front of the vehicle. Another sensor aperture may be located on the side of the vehicle and obtains either range and azimuth data or image data in order to determine velocity and track information on vehicles that pass the vehicle. These new systems must take all of the information from the multiple sensors apertures on the vehicle and compute an accurate picture of the moving objects around the vehicle; this is known as kinematic state of the targets, or Situation Awareness (SA). To do this the Situation Awareness Platform (SAP) must accurately align the sensors apertures to each other so that information about a target from one sensor aperture can be used with information about the target from a different sensor aperture. This is called Sensor Fusion (SF), this is necessary for the SAP to get an optimal kinematic state of the targets around the vehicle in order to assess threat. The sensor apertures must also be aligned to the body of the vehicle so that the SAP can determine the position and velocity of the target with respect to the vehicle; this is called Navigation Fusion (NF).
- One method of aligning the sensors apertures to each other and to the vehicle is to use mechanical and optical instruments, such as auto-collimators and laser boresight tools, during the production of the vehicle. This technique is not only costly, but would be require if a sensor aperture were repaired or replaced after production. An alignment procedure would have to be performed again in order to assure the safety critical systems were reporting accurately. Also as the vehicle goes through normal wear and tear the sensor apertures would start to become misaligned and may not be noticed by the operator. This means that the data from the sensor apertures would not correlate with each other and the vehicle reference frame until the sensor apertures were aligned again. Again, this would be costly to the vehicle operator and until performed, the SAP may not provide accurate data. Therefore, a method to align the sensor apertures to each other and to the vehicle without the use of sophisticated optical tools is required. This patent addresses this problem by describing methods that can be used to align the sensor apertures to each other and to the vehicle that do not require external alignment equipment.
- In a discussion of Prior Art, U.S. Pat. No. 5,245,909, Automatic Sensor Alignment, relates to systems for maintaining alignment-sensitive aircraft-borne avionics and weapons sensors in precise alignment. It further relates to methods for precisely aligning sensitive avionics for weapons system instrumentation, which is subject to vibrations causing misalignment. Whereas this disclosure relates to methods and systems that support advanced automotive systems not described in the prior art. A second key difference is the reliance of sensor data from the vehicle as part of the alignment method. Another difference is using image apertures with elements of the vehicle in the field of view of the imager and employing optical methods for determining changes to the alignment with respect to the vehicle and vehicle reference frame, then applying a compensation based on the misalignment angle measured. Finally, this system described herein does not require a reliance on boresighting and aligning any sensor to achieve a vehicle reference frame.
- U.S. Pat. No. 6,202,027, Automatic Curve Sensor Calibration, describes an improved system for accurately determining the travel path of a host vehicle and the azimuth angle of a target vehicle through an automatic calibration that detects and compensates for misalignment and curve sensor drift. The difference is a reliance on observed objects and track file generation and subsequent changes to the track files over time. Whereas this patent teaches methods of alignment based force vectors, rotational rates or optically measured changes with respect to the vehicle reference frame. Essentially all observed objects are compensated for misalignment error on the observing vehicle.
- U.S. Pat. No. 5,031,330, Electronic Boresight, teaches that pairs of level sensing devices can be used in a method that aligns plane surfaces to one another by tilting platforms equal to the amount misalignment measured to adjust the sensor azimuth. Whereas this patent teaches that the sensor apertures are rigidly mounted to the vehicle and correction to misalignment is done by compensation values observed with respect to the vehicle reference frame.
- Different sensors can be used in vehicles to identify objects and possible collision conditions. For example, there may be an optical sensor, such as a camera, mounted to the roof of the vehicle. Another Infrared (IR) sensor may be mounted in the front grill of the vehicle. A third inertial sensor may be located in yet another location in the central portion of the vehicle. Data from these different sensors is correlated together to identify and track objects that may come within a certain vicinity of the vehicle.
- The measurements from the different sensors must be translated to a common reference point before the different data can be accurately correlated. This translation is difficult because the sensors are positioned in different locations on the vehicle. For example, the sensor located inside the front bumper of the vehicle may move in one direction during a collision while the sensor located on the top of the vehicle roof may move in a different direction.
- One of the sensors may also experience vibrations at a different time than the other sensor. For example, the front bumper sensor may experience a vertical or horizontal movement when the vehicle runs over an obstacle before any movements or vibrations are experienced by the roof sensor. This different movements of sensors relative to each other make is very difficult to accurately determine the precise position and orientation of the sensors when the sensor readings are taken. This makes it difficult to translate the data into common reference coordinates.
- The present invention addresses this and other problems associated with the prior art.
- A vehicle sensor system configured to gather sensory data 360 degrees around the vehicle, comprising of sensor apertures for gathering data such as: range (e.g. ultrasonic); range and azimuth (e.g. laser and/or radar); images (e.g. optical and/or thermal). The vehicle has sensors that align and establish a vehicle reference frame by measuring body yaw, pitch and roll rates as well as acceleration along the 3 axes of the vehicle. The imaging apertures that have a clear view of body mold lines, like hood or rear deck, will align themselves to the vehicle reference frame, those apertures that can not align using optical methods are aligned to the vehicle using accelerometers and rates sensors by reading the inertial acceleration or angular rotation to align themselves to each other. An Integrated Computing Platform (ICP) hosts the SAP software that maintains complete system alignment by determining differences in alignment and applying or updating a compensation value with respect to the vehicle body coordinates resulting in a dynamically boresighted system.
- A multi-sensor system includes multiple sensors that are integrated onto the same substrate forming a unitary multi-sensor platform that provides a known consistent physical relationship between the multiple sensors. A processor can also be integrated onto the substrate so that data from the multiple sensors can be processed locally by the multi-sensor system.
- The foregoing and other objects, features and advantages of the invention will become more readily apparent from the following detailed description of a preferred embodiment of the invention which proceeds with reference to the accompanying drawings.
-
FIG. 1 is a diagram showing how a common inertial acceleration is sensed by accelerometers on each sensor and can be used to align the sensor coordinate frames. -
FIG. 2 is a diagram showing the pitch angles used to determine the pitch misalignment angle of the optical sensor. -
FIG. 3 is a diagram showing the yaw data that is used to determine the yaw misalignment angle of the optical sensor. -
FIG. 4 is a diagram showing the roll data that is used to determine the roll misalignment angle of the optical sensor. -
FIG. 5 is an image showing the top of the hood and how it is used to compute the pitch misalignment angle. -
FIG. 6 is a magnified image of the hood line showing the pixels of the image. -
FIG. 7 is an image showing the top of the hood and how it is used to compute the roll misalignment angle. -
FIG. 8 is a magnified image of the banked hood line showing the pixels of the image. -
FIG. 9 is an image showing the top of the hood and how it is used to compute the yaw misalignment angle. -
FIG. 10 is a flow chart that shows the alignment process when all sensors have micro-inertials. -
FIG. 11 is a flow chart that shows the alignment process when using micro-inertials and an optical sensor. -
FIG. 12 is a flow chart that shows the alignment process when all of the sensors are optical. -
FIG. 13 is a flow chart that shows the alignment when the sensors are on a common platform. -
FIG. 14 is a block diagram of a multi-sensor system. -
FIG. 15 is a block diagram of an alternate embodiment of the multi-sensor system that includes an on-board processor.FIG. 16 is a flow diagram showing how the processor inFIG. 15 operates. -
FIG. 17 is detailed diagram showing how different elements in the multi-sensor system are electrically connected together. -
FIG. 18 is a diagram showing how different multi-sensor systems operate together to track objects. - One method is to attach three axis accelerometers to each sensor and to the vehicle and use gravity and the acceleration of the vehicle, which will be sensed by the accelerometers, to align the sensor axes to each other and to the vehicle. Information from the vehicle that is available on the Car Area Network (CAN) bus will also be used to perform the calculation of the misalignment angles.
FIG. 1 shows in two dimensions the relation between sensor aperture A frame, sensor aperture B frame and the vehicle body reference frame. There are two accelerometers that sense acceleration in the X and Y axes of the sensor apertures and vehicle. This problem can easily be expanded to three dimensions with another accelerometer located in the Z-axes of each sensor and vehicle. - In
FIG. 1 the vehicle experiences a linear acceleration and this common acceleration is observed by the accelerometers located on sensor aperture A, sensor aperture B and the vehicle body. The accelerometers that are attached to the vehicle body are aligned to the vehicle body reference frame. By taking the difference in acceleration data from the accelerometers on sensor aperture A and sensor aperture B and inputting this data in a Kalman Filter, the misalignment angle between the two sensor apertures, .theta.sa-.theta.sb, can be computed. The same can be done between sensor aperture A and the vehicle body, and sensor aperture B and the vehicle body to compute all of the misalignment angles. This approach can be used to compute the three dimensional misalignment angles of roll, pitch and yaw between sensor apertures and the vehicle body reference frame. - The same approach can be used when the vehicle is turning and each accelerometer group experiences a centripetal acceleration. However, in this case the difference in accelerations must be compensated by the centripetal acceleration resulting from the lever arm vector between the two sensor apertures and the angular rotation of the vehicle. The angular rotation of the vehicle is sensed by a gyro triad or micro-inertial device located at the vehicle body reference frame Acomp=Asensora−wxwxR1 The input to the Kalman filter is now: Acomp−Asensorb where: Asensora is the acceleration measured by sensor A accelerometers Asensorb is the acceleration measured by sensor B accelerometers w is the angular rotation of the vehicle measured by the ref gyros x is the cross product of two vectors R1 is the lever arm vector between sensor A and sensor B Acomp is the sensor acceleration compensated for lever arm rotation.
- Also if the vehicle is stationary, the accelerometer groups will sense gravity and this can be used to help compute some of the misalignment angles. Information from the vehicle CAN bus, such as wheel rotation speeds are zero, will tell the Kalman filter that the vehicle is not moving and the only sensed acceleration will be from gravity.
-
FIG. 10 is a flow chart showing the process when all of the sensor apertures, as well as the vehicle body, have a micro-inertial attached to them. When the vehicle is moving, the micro-inertials sense the angular rotation and/or acceleration of the vehicle and this information is the input to a Kalman filter. The filter uses this information to estimate the roll, pitch and yaw misalignment angles between a sensor aperture and the vehicle body frame. These misalignment angles are then used to rotate the sensor target data into the vehicle body frame. With all of the target data in a common reference frame the processor can fuse data from several sensors into an optimal target track file. - The second method is to use accelerometers to align the sensor apertures to each other and one of the sensor apertures is aligned to the vehicle body by using optical information from the sensor aperture itself. For example, acceleration data can be used to align sensor aperture A to sensor aperture B, but sensor aperture B is aligned to the vehicle body directly by using sensor aperture B to compute the misalignment angles between sensor aperture B and the vehicle body. Since sensor aperture A is aligned to sensor aperture B and sensor aperture B is aligned to the vehicle body, you can compute the misalignment between sensor aperture A and the vehicle body. Sensor aperture B can be a visual sensor aperture, such as a video camera, and by observing the outline of the hood and body of the vehicle using this camera, you can compute the misalignment angles between sensor aperture B and the vehicle body frame.
-
FIG. 2 shows that the pitch misalignment angle is the angle between the sensor aperture's X-axis and vehicle's X-axis in the vertical plane. The pitch angle between the vehicle X-axis and a line from the sensor aperture to the top point of the hood, .PHI.vehicle, can be computed from the vehicle's dimensions. The image from the sensor aperture,FIG. 5 for example, shows the top of the hood. By counting the pixels from the center of the image down to the hood, Pp, the sensor aperture pitch angle can be computed. Using a 480.times.640 pixel image, this angle can be computed to within 1 pixel, seeFIG. 6 . With a vertical field of view, FOVv, the pitch angle is: .PHI.s=(Pp/480)*FOVv The pitch misalignment angle is: .PHI.misalign=.PHI.s−.PHI.vehicle. -
FIG. 3 shows that the small yaw misalignment angle is the angle between the sensor aperture's X axis and vehicle's X axis in the horizontal plane. The sensor aperture image shows the left and right edges of the hood,FIG. 9 . By computing the pixels from the left hood edge or mark on the hood to the left of the image border, Pyl, and the right hood edge or mark to the right border, Pyr, the yaw angle of the sensor aperture misalignment with a horizontal field of view, FOVh is: .PSI.Misalign=((Pyl−Pyr)/2*640)*FOVh. -
FIG. 4 shows that the small roll misalignment angle is the angle between the sensor aperture's Y-axis and vehicle's Y-axis in the vertical plane. The sensor aperture image shows that the hood line and the sensor aperture level lines cross to form the roll misalignment angle. This is shown inFIG. 7 . By measuring the pixels between the two lines at the edge of the image, Pr, the roll misalignment angle can be computed as follows: .THETA.Misalign=(2*Pr/640)*180/.pi.FIG. 8 shows that the hood line can be determined accurately to within a couple of pixels. -
FIG. 11 is a flow chart showing the process when at least one of the sensors is an optical device. All of the sensors have a micro-inertial attached to them. The optical device can see the targets and the outline of features of the vehicle, such as the hood line. The optical sensor uses the hood line information to compute the roll, pitch and yaw misalignment angles between the optical sensor frame and the vehicle body frame. - When the vehicle is moving, the micro-inertials sense the angular rotation and/or acceleration of the vehicle. Like
FIG. 10 , the Kalman filter estimates the roll, pitch and yaw misalignment angles between a sensor aperture frame and the optical sensor frame. These misalignment angles as well as the misalignment angles between the optical sensor and the vehicle body frame are then used to rotate all of the sensor target data into the vehicle body frame. Again, with all of the target data in a common reference frame the processor can fuse data from several sensors into an optimal target track file. - A third method is to use optical information from sensor aperture A and sensor aperture B to compute the misalignment between the two sensor apertures and to use optical information from sensor aperture B to compute the misalignment between sensor aperture B and the vehicle body. For example, sensor aperture A can be a ranging laser sensor aperture and it sends out multiply beams of light to detect a target. When the light is reflected from the target, sensor aperture B can also detect the reflected light in its video camera and using this information it can compute the misalignment between sensor aperture A and sensor aperture B.
-
FIG. 12 is a flow chart showing the process when all of the sensors on the vehicle are optical sensors. Each optical device can see targets and the outline of features of the vehicle, such as the hood or truck line. The optical sensors use this vehicle body information to compute the roll, pitch and yaw misalignment angles between the optical sensor frame and the vehicle body frame. These misalignment angles are then used to rotate the sensor target data from each sensor into the vehicle body frame. Like the two cases above, with all of the target data in a common reference frame the processor can fuse data from several sensors into an optimal target track file. - A fourth method is to collocate all of the sensor apertures into one box that is mounted on the vehicle, such as the roof, so that all sensor apertures are always aligned with respect to each other and the only alignment required is the alignment between this sensor aperture box and the vehicle body. This can be performed by using a set of accelerometers in the sensor aperture box and on the vehicle body frame or optically by using a video camera in the sensor aperture box.
-
FIG. 13 shows the case where all of the sensors are mounted onto one fixed platform. If one of the sensors is an optical sensor then it can be used to align the platform frame to the vehicle body frame as shown above. Once this set of misalignment angles is computed, then all of the target data from all of the sensors can be rotated to the common vehicle body reference frame. As shown above all of the target data is now in one reference frame for computing the optimal target tracks. If none of the sensors are optical, then a set of micro-inertials can be mounted on the common platform and also on the vehicle body. While the vehicle is moving the Kalman filter can now be used to compute the misalignment angles as discussed in the above paragraphs. - The systems described above can use dedicated processor systems, micro controllers, programmable logic devices, or microprocessors that perform some or all of the operations. Some of the operations described above may be implemented in software and other operations may be implemented in hardware.
- For the sake of convenience, the operations are described as various interconnected functional blocks or distinct software modules. This is not necessary, however, and there may be cases where these functional blocks or modules are equivalently aggregated into a single logic device, program or operation with unclear boundaries. In any event, the functional blocks and software modules or described features can be implemented by themselves, or in combination with other operations in either hardware or software.
- Having described and illustrated the principles of the invention in a preferred embodiment thereof, it should be apparent that the invention may be modified in arrangement and detail without departing from such principles. Claim is made to all modifications and variation coming within the spirit and scope of the following claims.
-
FIG. 14 shows a multi-sensor system 20812 that includes different sensors 20816 and 20818 that are both integrally attached to or integrally formed into thesubstrate 20814. Because the two sensors 20816 and 20818 are integrated onto thesame substrate 20814, any forces experienced by sensor 20816 are also experienced by sensor 20818. One type of material that is used forsubstrate 20814 is invar. Invar is a rigid metal that has been cured with respect to temperature so that its dimensions do not change with fluxuations in temperature. Any rigid material that is resilient to expansion or contraction with temperature changes can be used. - Locating the sensors 20816 and 20818 on the
same substrate 20814 simplifies the cost of sensor manufacturing and installation. For example, the two sensors 20816 can be assembled onto thesubstrate 20814 in a factory prior to being installed on a vehicle. If the two sensors 20816 and 20818 were not mounted on thesame substrate 20814, then each sensor would have to be separately mounted on the vehicle and then calibrated to a known alignment with respect to each other. Even if the two sensors were installed correctly, changes in the shape of the vehicle due to wear, temperature, etc. over time could change the initial alignment between the two sensors. - Premounting or prefabricating the sensors 20816 and 20818 on the
substrate 20814 prior to installation on a vehicle, prevents these alignment errors. Only the substrate 208 14 of the multi-sensor system 20812 has to be mounted to the vehicle, not the individual sensors 20816 and 20818. This allows the relative position 20820 and alignment between the two sensors 20816 and 20818 to remain the same regardless of how thesubstrate 20814 is mounted on the vehicle. - Wiring is also simplified since only one wiring harness has to be run through the vehicle to the multi-sensor system 20812.
- In one example, the sensor 20816 senses an area 20824 and the sensor 20818 senses an area 20822 that are both coincident. One of the sensors may have a wider field of view than the other sensor. There can also be more than two sensors on
substrate 20814 and any active or passive sensor that provides object detection or vehicle force measurements can be mounted ontosubstrate 20814. Some examples of sensors include ultrasonic, Infra-Red (IR), video, radar, and lidar sensors. - Depending on the
substrate 20814 and the types of sensors, different mounting techniques can be used. The sensors may be separate components that are glued or bolted onto thesubstrate 20814. If the multi-sensor system 20812 is an integrated circuit, then the sensors 20816 and 20818 may be integrally fabricated onto a silicon or alternative temperatureresilent substrate 20814 using known deposition processes. - In one example,
sensor 20814 is a radar or lidar sensor and sensor 20818 is a camera. Combining a video camera sensor with a radar and/or lidar sensor on the substrate 14 provides several advantages. The camera sensor 20818 provides good angle resolution and object identification. The radar or lidar sensor 20816 on the other hand is very effective in identifying range information. - Combining the camera video sensor 20818 with the radar or lidar sensor 20816 on the
same substrate 20814 allows more effective correlation of camera angle and identification data with radar or lidar range information. For example, theradar sensor 20814 may only be able to measure angle of an object to within one-half a degree. Because of the limited angle accuracy of the radar angle readings, it may not be possible to determine from the radar reading along if an oncoming vehicle is coming from the same lane of traffic or from an opposite lane of traffic. - The video sensor 20818 may be able to accurately determine the angle of an object to within one-tenth or one-one hundredth of a degree. By correlating the radar information with the camera information, the location of an on-coming vehicle can be determined more accurately.
- Do to vibration differences and possible inaccuracies in sensor alignment, it may not be possible, within fractional degrees of accuracy, to correlate information with separately mounted sensors. In other words, if the camera angle varies within plus or minus one degree with respect to the radar angle, then the camera data may not be able to refine the radar measurements.
- By mounting the camera sensor 20818 and the radar sensor 20816 to the
same substrate 20814, the relative position and alignment between the two sensors remains essentially the same regardless of physical effects on the vehicle. Thus, the camera data can be correlated with radar data to within fractions of a degree of accuracy. - In another example, a first sensor may detect one object out in front of the vehicle. A second sensor located somewhere else on the vehicle may detect two different objects in front of the vehicle. Because of vibrations in different parts of the vehicle, a central processor may not be able to determine which of the two objects detected by the second sensor is associated with the object detected by the first sensor. With the multi-sensor system 20812, measurement errors caused by this vehicle vibration is cancelled since the two sensors 20816 and 20818 effectively experience the same amount of vibration at the same time.
-
FIG. 14 shows an alternative embodiment where a processor 20826 is mounted to thesubstrate 20814. Again the processor 20826 can be a standalone component that is rigidly attached tosubstrate 20814. Alternatively, the processor 20826 is a portion of the same integrated circuit that also contains the circuitry for sensors 20816 and 20818. The processor 20826 can perform signal processing tasks for both sensor 20818 and sensor 20816 and can also handle communication and diagnostics tasks. Tracks for identified objects are sent overconnection 20828 to other multi-sensor systems in the vehicle or to a vehicle control system as shown later inFIG. 18 . - In previous multi-sensor applications, each sensor was required to send all data back to the same central processing system. This takes additional time and circuitry to send all of the data over a bus. By mounting the processor 20826 in the multi-sensor system 20812, data from both sensor 20816 and sensor 20818 can be processed locally requiring fewer reports to be sent over
connection 20828. - Referring to
FIG. 16 , the processor 20826 inFIG. 14 receives radar reports from the first sensor 20816 in block 20834. The processor 20826 receives image reports from the second sensor 20818 in block 20836. The processor 20826 correlates the different reports inblock 20838. Since the relative position of the two sensors 20816 and 20818 are the same and possibly coincident, the processor 20826 does not have to perform as many calculations transforming sensor measurements into common body coordinates for the vehicle. - The correlation may include first determining if the reports actually identify an object in block 20840. The processor 20826 can verify or refine object detection information from one of the sensors with the message reports received from the other sensor. If both sensors do not verify detection of the same object within some degree of certainty, then the processor system 20826 may discard the message reports or continue to analyze additional reports in block 20840.
- When an object is detected in block 20840, the processor 20826 only has to send one report in
block 20842 representing the information obtained from both sensor 20816 and sensor 20818. This reduces the total amount of data that has to be sent either to a central controller or another multi-sensor system inblock 20842. -
FIG. 17 shows in further detail the different devices that may be integrated on themulti-sensor substrate 20814.Camera optics 20850 and radar transmit/receivemodules 20852 are each connected to a Central Processing Unit (CPU) 20854 and a digital signal processor 20856. A memory 20858 is used to store sensor data, signal processing applications and other operating system functions. TheCPU 20854 is also used for conducting distributed sensor fusion as described in further detail below. - Referring to
FIG. 18 , different multi-sensor systems 20812A-20812D are used for monitoring different zones around avehicle 20860. For example, system 20812A monitorszone 1, system 20812B monitorszone 2, system 20812C monitorszone 3 and system 20812D monitorszone 4. TheCPU 20854 and digital signal processor 20856 (FIG. 17 ) in each multi-sensor system 20812A-20812D in combination with the camera and radar sensors identify and track objects autonomously, without having to communicate with acentral controller 20868 invehicle 20860. - Whenever an object is detected, identified and tracked, a track file is created for that object in memory 20858 (
FIG. 17 ). If the object moves to another zone around thevehicle 20860, the multi-sensor system for the zone where the object was previously detected only has to send the track files to the other multi-sensor system associated with the overlapping region. - For example, a
bicycle 20865 may be initially detected by multi-sensor system 20812A at location 20864A inzone 1. The multi-sensor system 20812A creates a track file containing position, speed, acceleration, range, angle, heading, etc. for thebike 20865. As thevehicle 20860 moves, or thebike 20865 moves, or both, thebike 20865 may move into a new position 20864B in an overlapping region 208 66 betweenzone 1 andzone 2. The multi-sensor system 20812A upon detecting thebike 20865 in theoverlapping region 20866 sends the latest track file for thebike 20865 to multi-sensor system 20812B overbus 20862. This allows the multi-sensor system 20812B to start actively trackingbike 20865 using the track information received from multi-sensor system 20812A. - The multi-sensor system 20812A only has to send a few of the latest track files for the
common area 20866 over connection 20864 to multi-sensor 20812B in order for system 20812B to maintain a track on bike 208 65. The track files can be exchanged between any of the multi-sensor systems 20812A-20812D. When there are two multi-sensor systems that have overlapping tracks for the same object, the track file with the greatest confidence of accuracy is used for vehicle warning, security, and control operations. There are known algorithms that calculate track files and calculate a degree of confidence in the track file calculations. Therefore, describing these algorithms will not be discussed in further detail. - There may be vibrational effects on the different multi-sensor systems 20812A-20812D. This however does not effect the track calculations generated by the individual multi-sensor systems 20812A-20812D. The only compensation for any vibration may be when the track files are translated into body coordinates when a possible control decision is made by the central controller 208 68.
- The
connection 20862 can a CAN bus, wireless 802.11 link or any other type of wired or wireless link. The system described above can use dedicated processor systems, micro controllers, programmable logic devices, or microprocessors that perform some or all of the operations. Some of the operations described above may be implemented in software and other operations may be implemented in hardware. - For the sake of convenience, the operations are described as various interconnected functional blocks or distinct software modules. This is not necessary, however, and there may be cases where these functional blocks or modules are equivalently aggregated into a single logic device, program or operation with unclear boundaries. In any event, the functional blocks and software modules or features of the flexible interface can be implemented by themselves, or in combination with other operations in either hardware or software.
Claims (13)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US14/617,815 US20150153376A1 (en) | 2004-11-09 | 2015-02-09 | Method and apparatus for the alignment of multi-aperture systems |
Applications Claiming Priority (5)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US10/985,577 US7337650B1 (en) | 2004-11-09 | 2004-11-09 | System and method for aligning sensors on a vehicle |
US12/024,058 US7681448B1 (en) | 2004-11-09 | 2008-01-31 | System and method for aligning sensors on a vehicle |
US12/698,960 US8001860B1 (en) | 2004-11-09 | 2010-02-02 | Method and apparatus for the alignment of multi-aperture systems |
US13/010,675 US8978439B1 (en) | 2004-11-09 | 2011-01-20 | System and apparatus for the alignment of multi-aperture systems |
US14/617,815 US20150153376A1 (en) | 2004-11-09 | 2015-02-09 | Method and apparatus for the alignment of multi-aperture systems |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/010,675 Continuation US8978439B1 (en) | 2004-11-09 | 2011-01-20 | System and apparatus for the alignment of multi-aperture systems |
Publications (1)
Publication Number | Publication Date |
---|---|
US20150153376A1 true US20150153376A1 (en) | 2015-06-04 |
Family
ID=39125321
Family Applications (5)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US10/985,577 Active 2024-12-21 US7337650B1 (en) | 2004-11-09 | 2004-11-09 | System and method for aligning sensors on a vehicle |
US12/024,058 Active 2024-12-03 US7681448B1 (en) | 2004-11-09 | 2008-01-31 | System and method for aligning sensors on a vehicle |
US12/698,960 Expired - Fee Related US8001860B1 (en) | 2004-11-09 | 2010-02-02 | Method and apparatus for the alignment of multi-aperture systems |
US13/010,675 Active 2027-09-13 US8978439B1 (en) | 2004-11-09 | 2011-01-20 | System and apparatus for the alignment of multi-aperture systems |
US14/617,815 Abandoned US20150153376A1 (en) | 2004-11-09 | 2015-02-09 | Method and apparatus for the alignment of multi-aperture systems |
Family Applications Before (4)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US10/985,577 Active 2024-12-21 US7337650B1 (en) | 2004-11-09 | 2004-11-09 | System and method for aligning sensors on a vehicle |
US12/024,058 Active 2024-12-03 US7681448B1 (en) | 2004-11-09 | 2008-01-31 | System and method for aligning sensors on a vehicle |
US12/698,960 Expired - Fee Related US8001860B1 (en) | 2004-11-09 | 2010-02-02 | Method and apparatus for the alignment of multi-aperture systems |
US13/010,675 Active 2027-09-13 US8978439B1 (en) | 2004-11-09 | 2011-01-20 | System and apparatus for the alignment of multi-aperture systems |
Country Status (1)
Country | Link |
---|---|
US (5) | US7337650B1 (en) |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111142082A (en) * | 2018-11-02 | 2020-05-12 | 现代自动车株式会社 | Apparatus and method for calibrating zero point of radar for vehicle |
US10698084B2 (en) * | 2017-06-16 | 2020-06-30 | Robert Bosch Gmbh | Method and system for carrying out a calibration of a sensor |
CN111578839A (en) * | 2020-05-25 | 2020-08-25 | 北京百度网讯科技有限公司 | Obstacle coordinate processing method and device, electronic equipment and readable storage medium |
US20210149020A1 (en) * | 2018-04-20 | 2021-05-20 | ZF Automotive UK Limited | A radar apparatus for a vehicle and method of detecting misalignment |
US20220179431A1 (en) * | 2020-12-04 | 2022-06-09 | Ship And Ocean Industries R&D Center | Assistance system for correcting vessel path and operation method thereof |
US11805390B2 (en) | 2018-12-05 | 2023-10-31 | Here Global B.V. | Method, apparatus, and computer program product for determining sensor orientation |
Families Citing this family (95)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7146260B2 (en) | 2001-04-24 | 2006-12-05 | Medius, Inc. | Method and apparatus for dynamic configuration of multiprocessor system |
US10298735B2 (en) | 2001-04-24 | 2019-05-21 | Northwater Intellectual Property Fund L.P. 2 | Method and apparatus for dynamic configuration of a multiprocessor health data system |
WO2006014974A2 (en) | 2004-07-26 | 2006-02-09 | Automotive Systems Laboratory, Inc. | Vulnerable road user protection system |
US7337650B1 (en) * | 2004-11-09 | 2008-03-04 | Medius Inc. | System and method for aligning sensors on a vehicle |
US7711483B2 (en) * | 2005-11-15 | 2010-05-04 | Sirf Technology, Inc. | Dead reckoning system |
US8494805B2 (en) | 2005-11-28 | 2013-07-23 | Orthosensor | Method and system for assessing orthopedic alignment using tracking sensors |
US8000926B2 (en) * | 2005-11-28 | 2011-08-16 | Orthosensor | Method and system for positional measurement using ultrasonic sensing |
US8098544B2 (en) | 2005-11-29 | 2012-01-17 | Orthosensor, Inc. | Method and system for enhancing accuracy in ultrasonic alignment |
US8864686B2 (en) * | 2005-12-01 | 2014-10-21 | Orthosensor Inc. | Virtual mapping of an anatomical pivot point and alignment therewith |
US8814810B2 (en) * | 2005-12-01 | 2014-08-26 | Orthosensor Inc. | Orthopedic method and system for mapping an anatomical pivot point |
US7779703B2 (en) * | 2006-06-15 | 2010-08-24 | The Boeing Company | System and method for aligning a device relative to a reference point of a vehicle |
US8421642B1 (en) | 2006-08-24 | 2013-04-16 | Navisense | System and method for sensorized user interface |
US8638296B1 (en) | 2006-09-05 | 2014-01-28 | Jason McIntosh | Method and machine for navigation system calibration |
US8086405B2 (en) * | 2007-06-28 | 2011-12-27 | Sirf Technology Holdings, Inc. | Compensation for mounting misalignment of a navigation device |
US8005563B2 (en) | 2007-10-26 | 2011-08-23 | The Boeing Company | System for assembling aircraft |
US8326587B2 (en) * | 2007-12-13 | 2012-12-04 | The Boeing Company | System, method, and computer program product for predicting cruise orientation of an as-built airplane |
US8733707B2 (en) | 2008-04-17 | 2014-05-27 | The Boeing Company | Line transfer system for airplane |
US7869895B2 (en) * | 2007-12-13 | 2011-01-11 | The Boeing Company | System, method, and computer program product for computing orientation alignment transfer tool locations to transfer predicted cruise orientation alignment of an as-built airplane |
US9189083B2 (en) | 2008-03-18 | 2015-11-17 | Orthosensor Inc. | Method and system for media presentation during operative workflow |
US8047047B2 (en) * | 2008-05-16 | 2011-11-01 | Honeywell International Inc. | Inertial sensor misalignment and compensation |
US8061181B2 (en) * | 2008-05-16 | 2011-11-22 | Honeywell International Inc. | Inertial sensor misalignment and compensation |
US9279882B2 (en) * | 2008-09-19 | 2016-03-08 | Caterpillar Inc. | Machine sensor calibration system |
US20100077860A1 (en) * | 2008-09-30 | 2010-04-01 | Honeywell International Inc. | Systems and methods for integrated isolator and transducer components in an inertial sensor |
DE102008042651B4 (en) | 2008-10-07 | 2022-02-24 | Robert Bosch Gmbh | Procedure for determining measurement deviations |
CN101769784B (en) * | 2008-12-27 | 2012-06-20 | 鸿富锦精密工业(深圳)有限公司 | Sensor assembly |
US8108147B1 (en) * | 2009-02-06 | 2012-01-31 | The United States Of America As Represented By The Secretary Of The Navy | Apparatus and method for automatic omni-directional visual motion-based collision avoidance |
US9358924B1 (en) | 2009-05-08 | 2016-06-07 | Eagle Harbor Holdings, Llc | System and method for modeling advanced automotive safety systems |
DE102009028072A1 (en) * | 2009-07-29 | 2011-02-10 | Robert Bosch Gmbh | Calibrating method for calibrating step counter fixed at e.g. belt of chronic sick patient for detecting movement of patient, during movement conditions in medical area, involves determining phase angle between for determining signal |
US8248301B2 (en) * | 2009-07-31 | 2012-08-21 | CSR Technology Holdings Inc. | Method and apparatus for using GPS satellite state computations in GLONASS measurement processing |
US8212874B2 (en) * | 2009-10-23 | 2012-07-03 | GM Global Technology Operations LLC | Automatic camera calibration using GPS and solar tracking |
US8566032B2 (en) * | 2009-10-30 | 2013-10-22 | CSR Technology Holdings Inc. | Methods and applications for altitude measurement and fusion of user context detection with elevation motion for personal navigation systems |
US8781737B2 (en) * | 2009-11-20 | 2014-07-15 | Qualcomm Incorporated | Spatial alignment determination for an inertial measurement unit (IMU) |
US9011448B2 (en) * | 2009-12-31 | 2015-04-21 | Orthosensor Inc. | Orthopedic navigation system with sensorized devices |
DE102010054066A1 (en) * | 2010-12-10 | 2012-06-14 | GM Global Technology Operations LLC | Method for operating a sensor of a vehicle and driver assistance system for a vehicle |
CA2769788C (en) * | 2011-03-23 | 2019-08-13 | Trusted Positioning Inc. | Methods of attitude and misalignment estimation for constraint free portable navigation |
US8775064B2 (en) * | 2011-05-10 | 2014-07-08 | GM Global Technology Operations LLC | Sensor alignment process and tools for active safety vehicle applications |
US8930063B2 (en) * | 2012-02-22 | 2015-01-06 | GM Global Technology Operations LLC | Method for determining object sensor misalignment |
US9165196B2 (en) * | 2012-11-16 | 2015-10-20 | Intel Corporation | Augmenting ADAS features of a vehicle with image processing support in on-board vehicle platform |
DE102013201379B4 (en) * | 2013-01-29 | 2020-12-10 | Robert Bosch Gmbh | Motorcycle with a camera system |
TW201443827A (en) * | 2013-05-03 | 2014-11-16 | Altek Autotronics Corp | Camera image calibrating system and method of calibrating camera image |
EP3076906B1 (en) * | 2013-12-05 | 2018-07-11 | Now Technologies Zrt. | Personal vehicle, and control apparatus and control method therefore |
US9898670B2 (en) * | 2013-12-13 | 2018-02-20 | Fts Computertechnik Gmbh | Method and device for observing the environment of a vehicle |
US10024955B2 (en) | 2014-03-28 | 2018-07-17 | GM Global Technology Operations LLC | System and method for determining of and compensating for misalignment of a sensor |
EP2933707B1 (en) * | 2014-04-14 | 2017-12-06 | iOnRoad Technologies Ltd. | Head mounted display presentation adjustment |
KR101579100B1 (en) | 2014-06-10 | 2015-12-22 | 엘지전자 주식회사 | Apparatus for providing around view and Vehicle including the same |
US9836966B2 (en) * | 2014-07-24 | 2017-12-05 | Gentex Corporation | Accelerometer integrated with display device |
CN106030431B (en) * | 2014-08-15 | 2017-11-03 | 深圳市大疆创新科技有限公司 | The automatic calibration system and method for sensor |
US9886040B1 (en) * | 2014-09-24 | 2018-02-06 | Rockwell Collins, Inc. | System and method for platform alignment, navigation or targeting |
US10890648B2 (en) * | 2014-10-24 | 2021-01-12 | Texas Instruments Incorporated | Method and apparatus for generating alignment matrix for camera-radar system |
FR3030091B1 (en) * | 2014-12-12 | 2018-01-26 | Airbus Operations | METHOD AND SYSTEM FOR AUTOMATICALLY DETECTING A DISALLIATION IN OPERATION OF A MONITORING SENSOR OF AN AIRCRAFT. |
US9886801B2 (en) * | 2015-02-04 | 2018-02-06 | GM Global Technology Operations LLC | Vehicle sensor compensation |
US9903945B2 (en) * | 2015-02-04 | 2018-02-27 | GM Global Technology Operations LLC | Vehicle motion estimation enhancement with radar data |
DE102015207375A1 (en) * | 2015-04-22 | 2016-10-27 | Robert Bosch Gmbh | Method and device for monitoring an area in front of a vehicle |
EP3158412B8 (en) * | 2015-05-23 | 2019-05-22 | SZ DJI Technology Co., Ltd. | Sensor fusion using inertial and image sensors |
US10088318B2 (en) * | 2015-08-27 | 2018-10-02 | Qualcomm Incorporated | Cradle rotation insensitive inertial navigation |
US10046713B2 (en) * | 2016-01-04 | 2018-08-14 | Ford Global Technologies, Llc | Sensor apparatus |
WO2017176689A1 (en) * | 2016-04-06 | 2017-10-12 | Karuza Andy | Proximity warning device |
US10365355B1 (en) | 2016-04-21 | 2019-07-30 | Hunter Engineering Company | Method for collective calibration of multiple vehicle safety system sensors |
DE102016208460A1 (en) * | 2016-05-18 | 2017-11-23 | Bayerische Motoren Werke Aktiengesellschaft | Position determining device for a single-track motor vehicle, single-track motor vehicle with position-determining device and method for determining the position |
US10520317B2 (en) | 2016-06-02 | 2019-12-31 | Maliszewski Family 2001 Trust | In-situ wheel position measurement using inertial measurement units (IMUs) |
US10338887B2 (en) | 2016-08-31 | 2019-07-02 | Hunter Engineering Company | Method for selective calibration of vehicle safety systems in response to vehicle alignment changes |
US10788316B1 (en) | 2016-09-21 | 2020-09-29 | Apple Inc. | Multi-sensor real-time alignment and calibration |
DE102016225579B4 (en) * | 2016-12-20 | 2022-11-17 | Audi Ag | Method for operating a sensor device of a motor vehicle and motor vehicle |
DE102016226312A1 (en) * | 2016-12-29 | 2018-07-05 | Robert Bosch Gmbh | Method for operating a driver assistance system for motor vehicles |
DE102017105209A1 (en) | 2017-03-13 | 2018-09-13 | Valeo Schalter Und Sensoren Gmbh | Determination of inclination angles with a laser scanner |
KR20190130614A (en) * | 2017-03-31 | 2019-11-22 | 에이캐럿큐브드 바이 에어버스 엘엘씨 | Vehicle monitoring system and method for detecting foreign objects |
US10272732B2 (en) * | 2017-05-31 | 2019-04-30 | GM Global Technology Operations LLC | Sensor linked suspension |
EP3642904A4 (en) * | 2017-06-22 | 2021-03-17 | Saab Ab | Arrangement and method for autoalignment of a stabilized subsystem |
US10809355B2 (en) * | 2017-07-18 | 2020-10-20 | Veoneer Us, Inc. | Apparatus and method for detecting alignment of sensor and calibrating antenna pattern response in an automotive detection system |
US20190120934A1 (en) * | 2017-10-19 | 2019-04-25 | GM Global Technology Operations LLC | Three-dimensional alignment of radar and camera sensors |
CN108279403B (en) * | 2018-01-04 | 2020-03-17 | 电子科技大学 | Keystone transformation parallel implementation method based on distance framing |
CN108318870B (en) * | 2018-03-07 | 2024-01-30 | 深圳市道通科技股份有限公司 | Vehicle-mounted radar calibration equipment |
US10388157B1 (en) | 2018-03-13 | 2019-08-20 | Allstate Insurance Company | Processing system having a machine learning engine for providing a customized driving assistance output |
GB2573015A (en) * | 2018-04-20 | 2019-10-23 | Trw Ltd | A radar apparatus for a vehicle and method of detecting misalignment |
US11597091B2 (en) * | 2018-04-30 | 2023-03-07 | BPG Sales and Technology Investments, LLC | Robotic target alignment for vehicle sensor calibration |
EP3788341B1 (en) | 2018-04-30 | 2024-02-07 | BPG Sales and Technology Investments, LLC | Vehicular alignment for sensor calibration |
US11781860B2 (en) | 2018-04-30 | 2023-10-10 | BPG Sales and Technology Investments, LLC | Mobile vehicular alignment for sensor calibration |
US11835646B2 (en) * | 2018-04-30 | 2023-12-05 | BPG Sales and Technology Investments, LLC | Target alignment for vehicle sensor calibration |
US11029390B2 (en) * | 2018-07-10 | 2021-06-08 | Ford Motor Company | Method and system for performing a vehicle height-radar alignment check to align a radar device provided in a vehicle |
US10948590B2 (en) * | 2018-07-26 | 2021-03-16 | GM Global Technology Operations LLC | Estimation and compensation of transceiver position offsets in a radar system for targets at unknown positions |
US10664997B1 (en) * | 2018-12-04 | 2020-05-26 | Almotive Kft. | Method, camera system, computer program product and computer-readable medium for camera misalignment detection |
DE102018221427B4 (en) * | 2018-12-11 | 2020-08-06 | Volkswagen Aktiengesellschaft | Method for determining an existing misalignment of at least one sensor within a sensor network |
EP3754359A1 (en) * | 2019-06-18 | 2020-12-23 | Zenuity AB | Method of determination of alignment angles of radar sensors for a road vehicle radar auto-alignment controller |
US11650415B2 (en) | 2019-12-16 | 2023-05-16 | Plusai, Inc. | System and method for a sensor protection mechanism |
US11724669B2 (en) | 2019-12-16 | 2023-08-15 | Plusai, Inc. | System and method for a sensor protection system |
US11077825B2 (en) | 2019-12-16 | 2021-08-03 | Plusai Limited | System and method for anti-tampering mechanism |
US11470265B2 (en) | 2019-12-16 | 2022-10-11 | Plusai, Inc. | System and method for sensor system against glare and control thereof |
US11754689B2 (en) | 2019-12-16 | 2023-09-12 | Plusai, Inc. | System and method for detecting sensor adjustment need |
US11313704B2 (en) | 2019-12-16 | 2022-04-26 | Plusai, Inc. | System and method for a sensor protection assembly |
US11738694B2 (en) * | 2019-12-16 | 2023-08-29 | Plusai, Inc. | System and method for anti-tampering sensor assembly |
DE102020109787A1 (en) | 2020-04-08 | 2021-10-14 | Valeo Schalter Und Sensoren Gmbh | Determining an angular position of a component of a motor vehicle |
JP2021196191A (en) * | 2020-06-10 | 2021-12-27 | セイコーエプソン株式会社 | Inertia sensor device and method for manufacturing inertia sensor device |
CN114067001B (en) * | 2022-01-14 | 2022-04-26 | 天津所托瑞安汽车科技有限公司 | Vehicle-mounted camera angle calibration method, terminal and storage medium |
US11772667B1 (en) | 2022-06-08 | 2023-10-03 | Plusai, Inc. | Operating a vehicle in response to detecting a faulty sensor using calibration parameters of the sensor |
DE102022128870B3 (en) | 2022-11-01 | 2024-02-08 | Marelli Automotive Lighting Reutlingen (Germany) GmbH | Method for determining misorientation of an inertial measuring unit of a vehicle and vehicle with an inertial measuring unit |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20030048357A1 (en) * | 2001-08-29 | 2003-03-13 | Geovantage, Inc. | Digital imaging system for airborne applications |
US20040257441A1 (en) * | 2001-08-29 | 2004-12-23 | Geovantage, Inc. | Digital imaging system for airborne applications |
Family Cites Families (287)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US2995318A (en) * | 1957-04-26 | 1961-08-08 | Chance Vought Corp | Optical data transfer system |
US4303978A (en) * | 1980-04-18 | 1981-12-01 | The Boeing Company | Integrated-strapdown-air-data sensor system |
DE3125161A1 (en) | 1981-06-26 | 1983-01-20 | Norbert 6500 Mainz Hinkel | System for providing motor vehicles with early warning of emergency service vehicles |
JPS5987597A (en) | 1982-11-11 | 1984-05-21 | 日産自動車株式会社 | Obstacle detector for vehicle |
US4591976A (en) | 1983-06-17 | 1986-05-27 | The United States Of America As Represented By The Secretary Of The Air Force | Multiple task oriented processor |
US4829434A (en) | 1987-04-29 | 1989-05-09 | General Motors Corporation | Adaptive vehicle |
DE3752122T3 (en) | 1987-05-09 | 2004-07-29 | Koninklijke Philips Electronics N.V. | Facility for receiving and processing road news reports |
US5031330A (en) | 1988-01-20 | 1991-07-16 | Kaiser Aerospace & Electronics Corporation | Electronic boresight |
US5045937A (en) | 1989-08-25 | 1991-09-03 | Space Island Products & Services, Inc. | Geographical surveying using multiple cameras to obtain split-screen images with overlaid geographical coordinates |
US5610815A (en) | 1989-12-11 | 1997-03-11 | Caterpillar Inc. | Integrated vehicle positioning and navigation system, apparatus and method |
US5648901A (en) | 1990-02-05 | 1997-07-15 | Caterpillar Inc. | System and method for generating paths in an autonomous vehicle |
GB9002951D0 (en) | 1990-02-09 | 1990-04-04 | Bowman Nigel J | Crash warning system |
US5008678A (en) | 1990-03-02 | 1991-04-16 | Hughes Aircraft Company | Electronically scanning vehicle radar sensor |
US5245909A (en) | 1990-05-07 | 1993-09-21 | Mcdonnell Douglas Corporation | Automatic sensor alignment |
US5111401A (en) | 1990-05-19 | 1992-05-05 | The United States Of America As Represented By The Secretary Of The Navy | Navigational control system for an autonomous vehicle |
JP2915080B2 (en) | 1990-05-25 | 1999-07-05 | 株式会社日立製作所 | Data processing method in multiprocessor system |
US5115245A (en) | 1990-09-04 | 1992-05-19 | Hughes Aircraft Company | Single substrate microwave radar transceiver including flip-chip integrated circuits |
JP3198514B2 (en) | 1990-12-27 | 2001-08-13 | 株式会社デンソー | GPS receiver for vehicles |
US5440644A (en) | 1991-01-09 | 1995-08-08 | Square D Company | Audio distribution system having programmable zoning features |
US6484080B2 (en) * | 1995-06-07 | 2002-11-19 | Automotive Technologies International Inc. | Method and apparatus for controlling a vehicular component |
US6738697B2 (en) * | 1995-06-07 | 2004-05-18 | Automotive Technologies International Inc. | Telematics system for vehicle diagnostics |
US5303297A (en) | 1991-07-25 | 1994-04-12 | Motorola, Inc. | Dynamic pricing method and apparatus for communication systems |
FR2682202B1 (en) | 1991-10-03 | 1994-03-11 | Sextant Avionique | METHOD AND DEVICE FOR REAL-TIME MANAGEMENT OF A SYSTEM COMPRISING AT LEAST ONE PROCESSOR CAPABLE OF MANAGING MULTIPLE FUNCTIONS. |
DE4219678A1 (en) | 1992-06-16 | 1994-01-05 | Eduard Kuehnert | Method and arrangement for securing people or objects |
IL100175A (en) | 1991-11-27 | 1994-11-11 | State Of Isreal Ministry Of De | Collision warning apparatus for a vehicle |
US6009330A (en) | 1992-01-27 | 1999-12-28 | Highwaymaster Communications, Inc. | Method and apparatus for call delivery to a mobile unit |
US5287199A (en) | 1992-02-27 | 1994-02-15 | At&T Bell Laboratories | Facsimile message processing and routing system |
US5438361A (en) * | 1992-04-13 | 1995-08-01 | Hughes Aircraft Company | Electronic gimbal system for electronically aligning video frames from a video sensor subject to disturbances |
EP0590588B2 (en) | 1992-09-30 | 2003-09-10 | Hitachi, Ltd. | Vehicle driving support system |
DE4237987B4 (en) | 1992-11-11 | 2004-07-22 | Adam Opel Ag | Electronic device |
US5339086A (en) | 1993-02-22 | 1994-08-16 | General Electric Co. | Phased array antenna with distributed beam steering |
JPH0717347A (en) | 1993-07-07 | 1995-01-20 | Mazda Motor Corp | Obstacle detecting device for automobile |
US5983161A (en) | 1993-08-11 | 1999-11-09 | Lemelson; Jerome H. | GPS vehicle collision avoidance warning and control system and method |
US6553130B1 (en) | 1993-08-11 | 2003-04-22 | Jerome H. Lemelson | Motor vehicle warning and control system and method |
JP3197403B2 (en) | 1993-09-07 | 2001-08-13 | 富士通株式会社 | Control method of computer system when application program failure occurs |
DE4334595C1 (en) | 1993-10-11 | 1995-04-27 | Siemens Ag | Control for a motor vehicle |
US6421429B1 (en) | 1993-12-29 | 2002-07-16 | At&T Corporation | Network-based system enabling image communications |
US5581462A (en) | 1994-01-06 | 1996-12-03 | Fairchild Space And Defense Corporation | Vehicle computer system and method |
US5500794A (en) | 1994-03-31 | 1996-03-19 | Panasonic Technologies, Inc. | Distribution system and method for menu-driven user interface |
US5440726A (en) | 1994-06-22 | 1995-08-08 | At&T Corp. | Progressive retry method and apparatus having reusable software modules for software failure recovery in multi-process message-passing applications |
US5948040A (en) | 1994-06-24 | 1999-09-07 | Delorme Publishing Co. | Travel reservation information and planning system |
US5572201A (en) | 1994-08-05 | 1996-11-05 | Federal Signal Corporation | Alerting device and system for abnormal situations |
US5532706A (en) | 1994-12-05 | 1996-07-02 | Hughes Electronics | Antenna array of radiators with plural orthogonal ports |
US6167253A (en) | 1995-01-12 | 2000-12-26 | Bell Atlantic Network Services, Inc. | Mobile data/message/electronic mail download system utilizing network-centric protocol such as Java |
US5577100A (en) | 1995-01-30 | 1996-11-19 | Telemac Cellular Corporation | Mobile phone with internal accounting |
KR960032262A (en) | 1995-02-09 | 1996-09-17 | 배순훈 | Vehicle safety system |
US5915214A (en) | 1995-02-23 | 1999-06-22 | Reece; Richard W. | Mobile communication service provider selection system |
JP3270801B2 (en) | 1995-04-11 | 2002-04-02 | 富士通株式会社 | Attenuator unit, step attenuator having the same, and electronic apparatus having step attenuator |
US5943427A (en) | 1995-04-21 | 1999-08-24 | Creative Technology Ltd. | Method and apparatus for three dimensional audio spatialization |
US5786998A (en) | 1995-05-22 | 1998-07-28 | Automated Monitoring And Control International, Inc. | Apparatus and method for tracking reporting and recording equipment inventory on a locomotive |
US6405132B1 (en) | 1997-10-22 | 2002-06-11 | Intelligent Technologies International, Inc. | Accident avoidance system |
EP1708151A3 (en) | 1995-08-09 | 2007-01-24 | Toyota Jidosha Kabushiki Kaisha | Travel plan preparing device |
JPH09142236A (en) | 1995-11-17 | 1997-06-03 | Mitsubishi Electric Corp | Periphery monitoring method and device for vehicle, and trouble deciding method and device for periphery monitoring device |
US5794164A (en) | 1995-11-29 | 1998-08-11 | Microsoft Corporation | Vehicle computer system |
JP3656301B2 (en) | 1995-12-28 | 2005-06-08 | 株式会社デンソー | Obstacle warning device for vehicles |
US5951620A (en) | 1996-01-26 | 1999-09-14 | Navigation Technologies Corporation | System and method for distributing information for storage media |
US20030212996A1 (en) | 1996-02-08 | 2003-11-13 | Wolzien Thomas R. | System for interconnection of audio program data transmitted by radio to remote vehicle or individual with GPS location |
US6343313B1 (en) | 1996-03-26 | 2002-01-29 | Pixion, Inc. | Computer conferencing system with real-time multipoint, multi-speed, multi-stream scalability |
US6028505A (en) | 1996-03-27 | 2000-02-22 | Clifford Electronics, Inc. | Electronic vehicle security system with remote control |
US6179489B1 (en) | 1997-04-04 | 2001-01-30 | Texas Instruments Incorporated | Devices, methods, systems and software products for coordination of computer main microprocessor and second microprocessor coupled thereto |
JPH09301068A (en) | 1996-05-13 | 1997-11-25 | Niles Parts Co Ltd | Electronic flasher device |
GB2313256B (en) | 1996-05-17 | 2000-08-23 | Motorola Ltd | Method and apparatus for system selection |
US5907293A (en) | 1996-05-30 | 1999-05-25 | Sun Microsystems, Inc. | System for displaying the characteristics, position, velocity and acceleration of nearby vehicles on a moving-map |
US6028537A (en) | 1996-06-14 | 2000-02-22 | Prince Corporation | Vehicle communication and remote control system |
US5793366A (en) | 1996-11-12 | 1998-08-11 | Sony Corporation | Graphical display of an animated data stream between devices on a bus |
JP3528440B2 (en) | 1996-07-17 | 2004-05-17 | 日産自動車株式会社 | In-vehicle information communication device |
JPH1076115A (en) | 1996-09-04 | 1998-03-24 | Shinei Sangyo Kk | Ceramic water purifying method |
US5966658A (en) | 1996-09-26 | 1999-10-12 | Highwaymaster Communications, Inc. | Automated selection of a communication path |
US6523696B1 (en) | 1996-10-15 | 2003-02-25 | Kabushiki Kaisha Toshiba | Communication control device for realizing uniform service providing environment |
US5959536A (en) | 1996-10-15 | 1999-09-28 | Philips Electronics North America Corporation | Task-driven distributed multimedia consumer system |
US7506020B2 (en) | 1996-11-29 | 2009-03-17 | Frampton E Ellis | Global network computers |
FR2756680B1 (en) | 1996-11-29 | 1999-02-12 | Sgs Thomson Microelectronics | HIGH DYNAMIC, LOW NOISE TRANSCONDUCTANCE AMPLIFIER |
US5923280A (en) | 1997-01-17 | 1999-07-13 | Automotive Systems Laboratory, Inc. | Vehicle collision radar with randomized FSK wave form |
US6240365B1 (en) | 1997-01-21 | 2001-05-29 | Frank E. Bunn | Automated vehicle tracking and service provision system |
US6009355A (en) | 1997-01-28 | 1999-12-28 | American Calcar Inc. | Multimedia information and control system for automobiles |
US6282714B1 (en) | 1997-01-31 | 2001-08-28 | Sharewave, Inc. | Digital wireless home computer system |
US6243772B1 (en) | 1997-01-31 | 2001-06-05 | Sharewave, Inc. | Method and system for coupling a personal computer with an appliance unit via a wireless communication link to provide an output display presentation |
JP3870983B2 (en) | 1997-02-17 | 2007-01-24 | ソニー株式会社 | Electronic device control apparatus and method, and electronic device |
EP0863606B1 (en) | 1997-03-05 | 2003-09-24 | Nec Corporation | Direct conversion receiver capable of cancelling DC offset voltages |
US6157921A (en) | 1998-05-01 | 2000-12-05 | Barnhill Technologies, Llc | Enhancing knowledge discovery using support vector machines in a distributed network environment |
US6298370B1 (en) | 1997-04-04 | 2001-10-02 | Texas Instruments Incorporated | Computer operating process allocating tasks between first and second processors at run time based upon current processor load |
US6105119A (en) | 1997-04-04 | 2000-08-15 | Texas Instruments Incorporated | Data transfer circuitry, DSP wrapper circuitry and improved processor devices, methods and systems |
US5909559A (en) | 1997-04-04 | 1999-06-01 | Texas Instruments Incorporated | Bus bridge device including data bus of first width for a first processor, memory controller, arbiter circuit and second processor having a different second data width |
US6408174B1 (en) | 1997-05-13 | 2002-06-18 | Telefonaktiebolaget Lm Ericsson (Publ) | Communication method, system, and device for reducing processor load at tariff switch |
CA2260762C (en) | 1997-05-19 | 2002-08-06 | Integrated Data Communications, Inc. | System and method to communicate time stamped, 3-axis geo-position data within telecommunication networks |
US6690681B1 (en) | 1997-05-19 | 2004-02-10 | Airbiquity Inc. | In-band signaling for data communications over digital wireless telecommunications network |
US6771629B1 (en) | 1999-01-15 | 2004-08-03 | Airbiquity Inc. | In-band signaling for synchronization in a voice communications network |
US6493338B1 (en) | 1997-05-19 | 2002-12-10 | Airbiquity Inc. | Multichannel in-band signaling for data communications over digital wireless telecommunications networks |
US7164662B2 (en) | 1997-05-19 | 2007-01-16 | Airbiquity, Inc. | Network delay identification method and apparatus |
US5956025A (en) | 1997-06-09 | 1999-09-21 | Philips Electronics North America Corporation | Remote with 3D organized GUI for a home entertainment system |
US6148261A (en) | 1997-06-20 | 2000-11-14 | American Calcar, Inc. | Personal communication system to send and receive voice data positioning information |
US6133853A (en) | 1998-07-30 | 2000-10-17 | American Calcar, Inc. | Personal communication and positioning system |
US7103834B1 (en) | 1997-06-25 | 2006-09-05 | Samsung Electronics Co., Ltd. | Method and apparatus for a home network auto-tree builder |
CA2401726C (en) | 1997-06-25 | 2010-10-19 | Richard James Humpleman | Browser based command and control home network |
EP0922201B1 (en) | 1997-07-01 | 2002-09-11 | Siemens Aktiengesellschaft | Navigation system for use in a vehicle |
US6275231B1 (en) | 1997-08-01 | 2001-08-14 | American Calcar Inc. | Centralized control and management system for automobiles |
EP0905960A1 (en) | 1997-08-07 | 1999-03-31 | Siemens Aktiengesellschaft | Method for billing for communications services |
US6707421B1 (en) | 1997-08-19 | 2004-03-16 | Siemens Vdo Automotive Corporation | Driver information system |
JPH1165436A (en) | 1997-08-21 | 1999-03-05 | Toyota Motor Corp | Data selection support device, and map data processing system and processor including same support device |
WO1999010876A1 (en) | 1997-08-21 | 1999-03-04 | Valeo Schalter Und Sensoren Gmbh | Sleeve for receiving a sensor, connected to the bumper of an automobile |
US5964822A (en) * | 1997-08-27 | 1999-10-12 | Delco Electronics Corp. | Automatic sensor azimuth alignment |
US6154123A (en) | 1997-09-05 | 2000-11-28 | Breed Automotive Technology, Inc. | Driver alertness monitoring system |
US6118860A (en) | 1997-09-12 | 2000-09-12 | Nortel Networks Corporation | Public communications services vending method and apparatus |
JPH11110700A (en) | 1997-09-29 | 1999-04-23 | Toyota Motor Corp | Intersection information providing system and on-vehicle information transmitter applied to the system |
US6032089A (en) | 1997-12-01 | 2000-02-29 | Chrysler Corporation | Vehicle instrument panel computer interface node |
US6163711A (en) | 1997-12-01 | 2000-12-19 | Nokia Mobile Phones, Ltd | Method and apparatus for interfacing a mobile phone with an existing audio system |
US6295541B1 (en) | 1997-12-16 | 2001-09-25 | Starfish Software, Inc. | System and methods for synchronizing two or more datasets |
US6032202A (en) | 1998-01-06 | 2000-02-29 | Sony Corporation Of Japan | Home audio/video network with two level device control |
US6038625A (en) | 1998-01-06 | 2000-03-14 | Sony Corporation Of Japan | Method and system for providing a device identification mechanism within a consumer audio/video network |
US6054950A (en) | 1998-01-26 | 2000-04-25 | Multispectral Solutions, Inc. | Ultra wideband precision geolocation system |
US6252544B1 (en) | 1998-01-27 | 2001-06-26 | Steven M. Hoffberg | Mobile communication device |
US6389340B1 (en) | 1998-02-09 | 2002-05-14 | Gary A. Rayner | Vehicle data recorder |
US5898392A (en) | 1998-02-10 | 1999-04-27 | Prince Corporation | System and method for remote control of an in-vehicle voice recorder and other electrical accessories |
DE19806557C2 (en) | 1998-02-17 | 2000-08-17 | Ericsson Telefon Ab L M | Display of charge information using the USSD mechanism |
US6374286B1 (en) | 1998-04-06 | 2002-04-16 | Rockwell Collins, Inc. | Real time processor capable of concurrently running multiple independent JAVA machines |
SE514332C2 (en) | 1998-04-30 | 2001-02-12 | Ehpt Sweden Ab | Procedure and apparatus for payment in a computer network |
JPH11321598A (en) | 1998-05-07 | 1999-11-24 | Honda Motor Co Ltd | Safety device for running of vehicle |
WO1999065183A2 (en) | 1998-06-05 | 1999-12-16 | British Telecommunications Public Limited Company | Accounting in a communications network |
AU4690899A (en) | 1998-06-18 | 2000-01-05 | Kline & Walker Llc | Automated devices to control equipment and machines with remote control and accountability worldwide |
US6292657B1 (en) | 1998-07-13 | 2001-09-18 | Openwave Systems Inc. | Method and architecture for managing a fleet of mobile stations over wireless data networks |
US6061709A (en) | 1998-07-31 | 2000-05-09 | Integrated Systems Design Center, Inc. | Integrated hardware and software task control executive |
US6185491B1 (en) | 1998-07-31 | 2001-02-06 | Sun Microsystems, Inc. | Networked vehicle controlling attached devices using JavaBeans™ |
US6377860B1 (en) | 1998-07-31 | 2002-04-23 | Sun Microsystems, Inc. | Networked vehicle implementing plug and play with javabeans |
US6199136B1 (en) | 1998-09-02 | 2001-03-06 | U.S. Philips Corporation | Method and apparatus for a low data-rate network to be represented on and controllable by high data-rate home audio/video interoperability (HAVi) network |
US5977906A (en) * | 1998-09-24 | 1999-11-02 | Eaton Vorad Technologies, L.L.C. | Method and apparatus for calibrating azimuth boresight in a radar system |
US6434447B1 (en) | 1998-10-02 | 2002-08-13 | Koninklijke Philips Electronics N.V. | Control property is mapped modally compatible GUI element |
US6060989A (en) | 1998-10-19 | 2000-05-09 | Lucent Technologies Inc. | System and method for preventing automobile accidents |
SE513210C2 (en) | 1998-10-30 | 2000-07-31 | Ericsson Telefon Ab L M | Procedure for determining movement data for objects |
EP1125415B1 (en) | 1998-11-02 | 2006-01-25 | Airbiquity Inc. | Geospacial internet protocol addressing |
US6748541B1 (en) | 1999-10-05 | 2004-06-08 | Aladdin Knowledge Systems, Ltd. | User-computer interaction method for use by a population of flexibly connectable computer systems |
US7047532B1 (en) | 1998-11-13 | 2006-05-16 | The Chase Manhattan Bank | Application independent messaging system |
US6522875B1 (en) | 1998-11-17 | 2003-02-18 | Eric Morgan Dowling | Geographical web browser, methods, apparatus and systems |
US6150961A (en) | 1998-11-24 | 2000-11-21 | International Business Machines Corporation | Automated traffic mapping |
US6169894B1 (en) | 1998-11-25 | 2001-01-02 | Lucent Technologies, Inc. | Apparatus, method and system for mobile broadcast of information specific to a geographic region |
US6567069B1 (en) | 1998-11-25 | 2003-05-20 | Alliedsignal Inc. | Integrated display and yoke mechanism |
EP1169792A4 (en) | 1998-12-23 | 2002-01-09 | American Calcar Inc | Technique for effective communications with, and provision of global positioning system (gps) based advertising information to, automobiles |
US6754485B1 (en) | 1998-12-23 | 2004-06-22 | American Calcar Inc. | Technique for effectively providing maintenance and information to vehicles |
US6806977B1 (en) | 1998-12-31 | 2004-10-19 | Automated Business Companies | Multiple integrated machine system |
US6445308B1 (en) | 1999-01-12 | 2002-09-03 | Toyota Jidosha Kabushiki Kaisha | Positional data utilizing inter-vehicle communication method and traveling control apparatus |
JP3555476B2 (en) | 1999-01-12 | 2004-08-18 | トヨタ自動車株式会社 | Travel control device for vehicles |
US6487717B1 (en) | 1999-01-15 | 2002-11-26 | Cummins, Inc. | System and method for transmission of application software to an embedded vehicle computer |
US6198996B1 (en) | 1999-01-28 | 2001-03-06 | International Business Machines Corporation | Method and apparatus for setting automotive performance tuned preferences set differently by a driver |
DE19909157A1 (en) | 1999-03-02 | 2000-09-21 | Daimler Chrysler Ag | Distributed vehicle information processing and control system |
US6161071A (en) | 1999-03-12 | 2000-12-12 | Navigation Technologies Corporation | Method and system for an in-vehicle computing architecture |
JP4258585B2 (en) | 1999-03-19 | 2009-04-30 | 株式会社エクォス・リサーチ | Destination setting device |
US6097285A (en) | 1999-03-26 | 2000-08-01 | Lucent Technologies Inc. | Automotive auditory feedback of changing conditions outside the vehicle cabin |
US6181994B1 (en) | 1999-04-07 | 2001-01-30 | International Business Machines Corporation | Method and system for vehicle initiated delivery of advanced diagnostics based on the determined need by vehicle |
US6233468B1 (en) | 1999-04-09 | 2001-05-15 | E. Lead Electronic Co., Ltd. | Hand-free system capable of preventing a vehicle's automatic antenna from random operation |
SE514264C2 (en) | 1999-05-07 | 2001-01-29 | Ericsson Telefon Ab L M | A communication system |
DE19922608A1 (en) | 1999-05-17 | 2000-11-23 | Media Praesent Ursula Nitzsche | Wireless emergency signal transmission, especially to or between vehicles, involves using predefined coded RDS format message at low power in VHF radio band, preferably using free frequency |
WO2000072463A2 (en) | 1999-05-26 | 2000-11-30 | Johnson Controls Interiors Technology Corp. | Wireless communications system and method |
US6182006B1 (en) | 1999-06-01 | 2001-01-30 | Navigation Technologies Corporation | Navigation system remote control unit with data caddy functionality |
AU5181700A (en) | 1999-06-01 | 2000-12-18 | Siemens Automotive Corporation | Portable driver information device |
US6266617B1 (en) | 1999-06-10 | 2001-07-24 | Wayne W. Evans | Method and apparatus for an automatic vehicle location, collision notification and synthetic voice |
US6754183B1 (en) | 1999-06-14 | 2004-06-22 | Sun Microsystems, Inc. | System and method for integrating a vehicle subnetwork into a primary network |
US6430164B1 (en) | 1999-06-17 | 2002-08-06 | Cellport Systems, Inc. | Communications involving disparate protocol network/bus and device subsystems |
US6571136B1 (en) | 1999-06-19 | 2003-05-27 | International Business Machines Corporation | Virtual network adapter |
JP3515926B2 (en) | 1999-06-23 | 2004-04-05 | 本田技研工業株式会社 | Vehicle periphery monitoring device |
DE19931161A1 (en) | 1999-07-06 | 2001-01-11 | Volkswagen Ag | Distance-sensitive, speed-controlled motor-vehicle road travel method, involves evaluation of predictive data about road conditions |
DE60008023T2 (en) | 1999-07-15 | 2004-09-02 | Richard B. Himmelstein | COMMUNICATION DEVICE FOR EFFICIENT ACCESS TO DATA FROM THE INTERNET |
US7272637B1 (en) | 1999-07-15 | 2007-09-18 | Himmelstein Richard B | Communication system and method for efficiently accessing internet resources |
US6166627A (en) | 1999-07-20 | 2000-12-26 | Reeley; Ronald B. | Mobile detection and alert system |
EP1071228B1 (en) | 1999-07-20 | 2009-04-15 | Texas Instruments Inc. | Wireless network with steerable antenna calibration over independent control path |
US6952155B2 (en) | 1999-07-23 | 2005-10-04 | Himmelstein Richard B | Voice-controlled security system with proximity detector |
US8648692B2 (en) | 1999-07-23 | 2014-02-11 | Seong Sang Investments Llc | Accessing an automobile with a transponder |
US6496107B1 (en) | 1999-07-23 | 2002-12-17 | Richard B. Himmelstein | Voice-controlled vehicle control system |
US7080050B1 (en) | 1999-08-05 | 2006-07-18 | Barter Securities | Electronic bartering system |
US6429789B1 (en) | 1999-08-09 | 2002-08-06 | Ford Global Technologies, Inc. | Vehicle information acquisition and display assembly |
JP3788203B2 (en) | 1999-08-10 | 2006-06-21 | 日産自動車株式会社 | Hand-free telephone equipment for automobiles |
US6297732B2 (en) | 1999-09-08 | 2001-10-02 | Precision Navigation, Inc. | Radar/laser detection device with multi-sensing and reporting capability |
US6647270B1 (en) | 1999-09-10 | 2003-11-11 | Richard B. Himmelstein | Vehicletalk |
US6754690B2 (en) | 1999-09-16 | 2004-06-22 | Honeywell, Inc. | Method for time partitioned application scheduling in a computer operating system |
US7484008B1 (en) | 1999-10-06 | 2009-01-27 | Borgia/Cummins, Llc | Apparatus for vehicle internetworks |
US6574610B1 (en) | 1999-10-19 | 2003-06-03 | Motorola, Inc. | Trusted elements within a distributed bandwidth system |
JP2003529054A (en) | 1999-10-19 | 2003-09-30 | アメリカン カルカー インコーポレイティド | Effective navigation technology based on user preferences |
US6614349B1 (en) | 1999-12-03 | 2003-09-02 | Airbiquity Inc. | Facility and method for tracking physical assets |
EP1107522B1 (en) | 1999-12-06 | 2010-06-16 | Telefonaktiebolaget LM Ericsson (publ) | Intelligent piconet forming |
US7024363B1 (en) | 1999-12-14 | 2006-04-04 | International Business Machines Corporation | Methods and apparatus for contingent transfer and execution of spoken language interfaces |
US6559773B1 (en) | 1999-12-21 | 2003-05-06 | Visteon Global Technologies, Inc. | Reconfigurable display architecture with spontaneous reconfiguration |
JP2001195699A (en) | 2000-01-14 | 2001-07-19 | Yazaki Corp | Vehicle circumference monitor device and recording medium for stored with vehicle collision danger judgement processing program |
GB2358766B (en) | 2000-01-26 | 2004-03-31 | Hewlett Packard Co | Cost-sensitive control of data transfer involving a mobile entity |
US6326903B1 (en) | 2000-01-26 | 2001-12-04 | Dave Gross | Emergency vehicle traffic signal pre-emption and collision avoidance system |
EP1264464A2 (en) | 2000-02-03 | 2002-12-11 | Openwave Systems (ROI) Limited | A network-based billing method and system |
IT1319895B1 (en) | 2000-02-08 | 2003-11-12 | Bottero Spa | GROUP FOR THE CLASSIFICATION AND TRANSFER OF GLASS SHEETS. |
AU5015700A (en) | 2000-03-21 | 2001-10-03 | Airbiquity Inc | Voiceband modem for data communications over digital wireless networks |
US7187947B1 (en) | 2000-03-28 | 2007-03-06 | Affinity Labs, Llc | System and method for communicating selected information to an electronic device |
US6980092B2 (en) | 2000-04-06 | 2005-12-27 | Gentex Corporation | Vehicle rearview mirror assembly incorporating a communication system |
US6937732B2 (en) | 2000-04-07 | 2005-08-30 | Mazda Motor Corporation | Audio system and its contents reproduction method, audio apparatus for a vehicle and its contents reproduction method, portable audio apparatus, computer program product and computer-readable storage medium |
US6785551B1 (en) | 2000-04-07 | 2004-08-31 | Ford Motor Company | Method of providing dynamic regionally relevant data to a mobile environment |
US6292747B1 (en) | 2000-04-20 | 2001-09-18 | International Business Machines Corporation | Heterogeneous wireless network for traveler information |
US7000469B2 (en) * | 2000-04-21 | 2006-02-21 | Intersense, Inc. | Motion-tracking |
US20020144010A1 (en) | 2000-05-09 | 2002-10-03 | Honeywell International Inc. | Communication handling in integrated modular avionics |
US20020012329A1 (en) | 2000-06-02 | 2002-01-31 | Timothy Atkinson | Communications apparatus interface and method for discovery of remote devices |
US7006950B1 (en) | 2000-06-12 | 2006-02-28 | Siemens Corporate Research, Inc. | Statistical modeling and performance characterization of a real-time dual camera surveillance system |
US6417782B1 (en) | 2000-06-22 | 2002-07-09 | Larry Dean Darnall | Driver's emergency alert system |
US7089206B2 (en) | 2000-06-30 | 2006-08-08 | Ubs Ag | Trade allocation |
US6445983B1 (en) | 2000-07-07 | 2002-09-03 | Case Corporation | Sensor-fusion navigator for automated guidance of off-road vehicles |
US7375728B2 (en) | 2001-10-01 | 2008-05-20 | University Of Minnesota | Virtual mirror |
US6725031B2 (en) | 2000-07-21 | 2004-04-20 | Telemac Corporation | Method and system for data rating for wireless devices |
US6816458B1 (en) | 2000-09-13 | 2004-11-09 | Harris Corporation | System and method prioritizing message packets for transmission |
US7158956B1 (en) | 2000-09-20 | 2007-01-02 | Himmelstein Richard B | Electronic real estate bartering system |
US6362748B1 (en) | 2000-09-27 | 2002-03-26 | Lite Vision Corporation | System for communicating among vehicles and a communication system control center |
US6580973B2 (en) | 2000-10-14 | 2003-06-17 | Robert H. Leivian | Method of response synthesis in a driver assistance system |
JP3834463B2 (en) | 2000-10-13 | 2006-10-18 | 株式会社日立製作所 | In-vehicle failure alarm reporting system |
US6756998B1 (en) | 2000-10-19 | 2004-06-29 | Destiny Networks, Inc. | User interface and method for home automation system |
JP3837282B2 (en) | 2000-10-24 | 2006-10-25 | 株式会社ケーヒン | Fuel injection valve |
USD448366S1 (en) | 2000-10-31 | 2001-09-25 | Airbiquity Inc. | Accessory module for a cellular telephone handset |
US20020086706A1 (en) | 2000-11-15 | 2002-07-04 | Ming-Feng Chen | Mobile device server |
US20020105423A1 (en) | 2000-12-05 | 2002-08-08 | Rast Rodger H. | Reaction advantage anti-collision systems and methods |
US20020070852A1 (en) | 2000-12-12 | 2002-06-13 | Pearl I, Llc | Automobile display control system |
US20020083143A1 (en) | 2000-12-13 | 2002-06-27 | Philips Electronics North America Corporation | UPnP architecture for heterogeneous networks of slave devices |
JP2002189075A (en) | 2000-12-20 | 2002-07-05 | Fujitsu Ten Ltd | Method for detecting stationary on-road object |
US7165109B2 (en) | 2001-01-12 | 2007-01-16 | Microsoft Corporation | Method and system to access software pertinent to an electronic peripheral device based on an address stored in a peripheral device |
WO2002071287A2 (en) | 2001-02-23 | 2002-09-12 | Mobilitec Inc. | System and method for charging for directed provisioning of user applications on limited-resource devices |
US7171189B2 (en) | 2001-02-28 | 2007-01-30 | Nortel Networks Limited | Location based billing of data services in a mobile telecommunication system |
US6734799B2 (en) | 2001-03-01 | 2004-05-11 | Trw Inc. | Apparatus and method for responding to the health and fitness of a driver of a vehicle |
US7120129B2 (en) | 2001-03-13 | 2006-10-10 | Microsoft Corporation | System and method for achieving zero-configuration wireless computing and computing device incorporating same |
US6708100B2 (en) | 2001-03-14 | 2004-03-16 | Raytheon Company | Safe distance algorithm for adaptive cruise control |
US6496117B2 (en) | 2001-03-30 | 2002-12-17 | Koninklijke Philips Electronics N.V. | System for monitoring a driver's attention to driving |
US7073044B2 (en) | 2001-03-30 | 2006-07-04 | Intel Corporation | Method and apparatus for sharing TLB entries |
US6895238B2 (en) | 2001-03-30 | 2005-05-17 | Motorola, Inc. | Method for providing entertainment to a portable device |
US7146260B2 (en) | 2001-04-24 | 2006-12-05 | Medius, Inc. | Method and apparatus for dynamic configuration of multiprocessor system |
US6629033B2 (en) | 2001-04-24 | 2003-09-30 | Medius, Inc. | Open communication system for real-time multiprocessor applications |
AU2002347941A1 (en) | 2001-06-15 | 2003-01-02 | Carcheckup, Llc | Auto diagnosis method and device |
US6973030B2 (en) | 2001-06-20 | 2005-12-06 | Motorola, Inc. | Method and apparatus for controlling multiple logical data flow in a variable data rate environment |
US7283567B2 (en) | 2001-06-22 | 2007-10-16 | Airbiquity Inc. | Network delay identification method and apparatus |
US6792351B2 (en) | 2001-06-26 | 2004-09-14 | Medius, Inc. | Method and apparatus for multi-vehicle communication |
US6778073B2 (en) | 2001-06-26 | 2004-08-17 | Medius, Inc. | Method and apparatus for managing audio devices |
US6615137B2 (en) | 2001-06-26 | 2003-09-02 | Medius, Inc. | Method and apparatus for transferring information between vehicles |
US6641087B1 (en) | 2001-10-09 | 2003-11-04 | Cubic Defense Systems, Inc. | Anti-hijacking system operable in emergencies to deactivate on-board flight controls and remotely pilot aircraft utilizing autopilot |
US7283904B2 (en) | 2001-10-17 | 2007-10-16 | Airbiquity, Inc. | Multi-sensor fusion |
US7099796B2 (en) | 2001-10-22 | 2006-08-29 | Honeywell International Inc. | Multi-sensor information fusion technique |
US8489063B2 (en) | 2001-10-24 | 2013-07-16 | Sipco, Llc | Systems and methods for providing emergency messages to a mobile device |
US7480501B2 (en) | 2001-10-24 | 2009-01-20 | Statsignal Ipc, Llc | System and method for transmitting an emergency message over an integrated wireless network |
US7215965B2 (en) | 2001-11-01 | 2007-05-08 | Airbiquity Inc. | Facility and method for wireless transmission of location data in a voice channel of a digital wireless telecommunications network |
US6778924B2 (en) * | 2001-11-06 | 2004-08-17 | Honeywell International Inc. | Self-calibrating inertial measurement system method and apparatus |
US20030158614A1 (en) | 2002-02-18 | 2003-08-21 | Friel Joseph T | Audio system for vehicle with battery-backed storage |
US8559951B2 (en) | 2002-03-13 | 2013-10-15 | Intellectual Ventures I Llc | Method and apparatus for performing handover in a bluetooth radiocommunication system |
DE10217294A1 (en) * | 2002-04-18 | 2003-11-06 | Sick Ag | sensor orientation |
US6771208B2 (en) * | 2002-04-24 | 2004-08-03 | Medius, Inc. | Multi-sensor system |
US7178049B2 (en) | 2002-04-24 | 2007-02-13 | Medius, Inc. | Method for multi-tasking multiple Java virtual machines in a secure environment |
US6829568B2 (en) | 2002-04-26 | 2004-12-07 | Simon Justin Julier | Method and apparatus for fusing signals with partially known independent error components |
US7269188B2 (en) | 2002-05-24 | 2007-09-11 | Airbiquity, Inc. | Simultaneous voice and data modem |
US6782315B2 (en) * | 2002-06-19 | 2004-08-24 | Ford Global Technologies, Llc | Method and apparatus for compensating misalignments of a sensor system used in a vehicle dynamic control system |
US20040029545A1 (en) | 2002-08-09 | 2004-02-12 | Anderson Jon J. | Method and system for leaving a communication channel in a wireless communications system |
USD479228S1 (en) | 2002-09-03 | 2003-09-02 | Airbiquity Inc. | Hands-free kit for mounting a wireless device in a vehicle |
GB0227672D0 (en) | 2002-11-27 | 2003-01-08 | Ricardo Consulting Eng | Improved engine management |
US7379707B2 (en) | 2004-08-26 | 2008-05-27 | Raysat Antenna Systems, L.L.C. | System for concurrent mobile two-way data communications and TV reception |
EP1602117B2 (en) * | 2003-02-21 | 2015-11-18 | Gentex Corporation | Automatic vehicle exterior light control system assemblies |
US7239949B2 (en) * | 2003-02-26 | 2007-07-03 | Ford Global Technologies, Llc | Integrated sensing system |
US6906619B2 (en) | 2003-02-27 | 2005-06-14 | Motorola, Inc. | Visual attention influenced condition indicia apparatus and method |
JP3928571B2 (en) | 2003-03-14 | 2007-06-13 | トヨタ自動車株式会社 | Vehicle driving assistance device |
US7263332B1 (en) | 2003-04-09 | 2007-08-28 | Cool & Useful Products, Llc | Methods and apparatus for communicating in a vehicle and other radio environments |
US7079993B2 (en) | 2003-04-29 | 2006-07-18 | Daniel H. Wagner Associates, Inc. | Automated generator of optimal models for the statistical analysis of data |
US7343160B2 (en) | 2003-09-29 | 2008-03-11 | Broadcom Corporation | System and method for servicing communications using both fixed and mobile wireless networks |
US20050070221A1 (en) | 2003-09-30 | 2005-03-31 | Upton Michael P. | Vehicular repeater multi-unit system and method for allowing the first vehicular repeater unit on-scene to remain priority |
US7689321B2 (en) | 2004-02-13 | 2010-03-30 | Evolution Robotics, Inc. | Robust sensor fusion for mapping and localization in a simultaneous localization and mapping (SLAM) system |
ES2238936B1 (en) | 2004-02-27 | 2006-11-16 | INSTITUTO NACIONAL DE TECNICA AEROESPACIAL "ESTEBAN TERRADAS" | SYSTEM AND METHOD OF FUSION OF SENSORS TO ESTIMATE POSITION, SPEED AND ORIENTATION OF A VEHICLE, ESPECIALLY AN AIRCRAFT. |
US7289906B2 (en) | 2004-04-05 | 2007-10-30 | Oregon Health & Science University | Navigation system applications of sigma-point Kalman filters for nonlinear estimation and sensor fusion |
US7260501B2 (en) | 2004-04-21 | 2007-08-21 | University Of Connecticut | Intelligent model-based diagnostics for system monitoring, diagnosis and maintenance |
US20050260984A1 (en) | 2004-05-21 | 2005-11-24 | Mobile Satellite Ventures, Lp | Systems and methods for space-based use of terrestrial cellular frequency spectrum |
US7614055B2 (en) | 2004-06-14 | 2009-11-03 | Alcatel-Lucent Usa Inc. | Selecting a processor to run an executable of a distributed software application upon startup of the distributed software application |
JP5306652B2 (en) | 2004-11-03 | 2013-10-02 | ティジックス インコーポレイテッド | Integrated image processor |
US7337650B1 (en) * | 2004-11-09 | 2008-03-04 | Medius Inc. | System and method for aligning sensors on a vehicle |
EP1851565B1 (en) | 2005-01-18 | 2009-10-14 | Marinvent Corporation | Method and apparatus for performing a sensor fusion to provide a position of a target-of-interest |
US7508810B2 (en) | 2005-01-31 | 2009-03-24 | Airbiquity Inc. | Voice channel control of wireless packet data communications |
US8014942B2 (en) | 2005-06-15 | 2011-09-06 | Airbiquity, Inc. | Remote destination programming for vehicle navigation |
US8254301B2 (en) | 2005-11-22 | 2012-08-28 | Telcordia Technologies, Inc. | Group-header based method to organize local peer group of vehicles for inter-vehicle communication |
US8554920B2 (en) | 2005-11-22 | 2013-10-08 | Telcordia Technologies, Inc. | Linked equivalent cell header-based approach and protocol for organizing an ad-hoc network |
US7924934B2 (en) | 2006-04-07 | 2011-04-12 | Airbiquity, Inc. | Time diversity voice channel data communications |
US20070260373A1 (en) | 2006-05-08 | 2007-11-08 | Langer William J | Dynamic vehicle durability testing and simulation |
US20070260372A1 (en) | 2006-05-08 | 2007-11-08 | Langer William J | Dynamic vehicle suspension system testing and simulation |
US8108092B2 (en) | 2006-07-14 | 2012-01-31 | Irobot Corporation | Autonomous behaviors for a remote vehicle |
US7979858B2 (en) | 2006-10-03 | 2011-07-12 | Sas Institute Inc. | Systems and methods for executing a computer program that executes multiple processes in a multi-processor environment |
US7579942B2 (en) | 2006-10-09 | 2009-08-25 | Toyota Motor Engineering & Manufacturing North America, Inc. | Extra-vehicular threat predictor |
DE102007007266B4 (en) | 2007-02-14 | 2016-02-25 | Airbus Defence and Space GmbH | Method for evaluating sensor measured values |
US20090090592A1 (en) | 2007-10-05 | 2009-04-09 | Gm Global Technology Operations, Inc. | High-Frequency Anti-Lock Clutch System and Method |
KR101293069B1 (en) | 2007-10-20 | 2013-08-06 | 에어비퀴티 인코포레이티드. | Wireless in-band signaling with in-vehicle systems |
US8636670B2 (en) | 2008-05-13 | 2014-01-28 | The Invention Science Fund I, Llc | Circulatory monitoring systems and methods |
EP2266092A4 (en) | 2008-03-04 | 2011-07-06 | Univ Sydney | Method and system for exploiting information from heterogeneous sources |
EP2107503A1 (en) | 2008-03-31 | 2009-10-07 | Harman Becker Automotive Systems GmbH | Method and device for generating a real time environment model for vehicles |
US8260515B2 (en) | 2008-07-24 | 2012-09-04 | GM Global Technology Operations LLC | Adaptive vehicle control system with driving style recognition |
US7983310B2 (en) | 2008-09-15 | 2011-07-19 | Airbiquity Inc. | Methods for in-band signaling through enhanced variable-rate codecs |
US8244408B2 (en) | 2009-03-09 | 2012-08-14 | GM Global Technology Operations LLC | Method to assess risk associated with operating an autonomic vehicle control system |
US8073440B2 (en) | 2009-04-27 | 2011-12-06 | Airbiquity, Inc. | Automatic gain control in a personal navigation device |
US8838332B2 (en) | 2009-10-15 | 2014-09-16 | Airbiquity Inc. | Centralized management of motor vehicle software applications and services |
US8204927B1 (en) | 2010-03-15 | 2012-06-19 | California Institute Of Technology | System and method for cognitive processing for data fusion |
-
2004
- 2004-11-09 US US10/985,577 patent/US7337650B1/en active Active
-
2008
- 2008-01-31 US US12/024,058 patent/US7681448B1/en active Active
-
2010
- 2010-02-02 US US12/698,960 patent/US8001860B1/en not_active Expired - Fee Related
-
2011
- 2011-01-20 US US13/010,675 patent/US8978439B1/en active Active
-
2015
- 2015-02-09 US US14/617,815 patent/US20150153376A1/en not_active Abandoned
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20030048357A1 (en) * | 2001-08-29 | 2003-03-13 | Geovantage, Inc. | Digital imaging system for airborne applications |
US20040257441A1 (en) * | 2001-08-29 | 2004-12-23 | Geovantage, Inc. | Digital imaging system for airborne applications |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10698084B2 (en) * | 2017-06-16 | 2020-06-30 | Robert Bosch Gmbh | Method and system for carrying out a calibration of a sensor |
US20210149020A1 (en) * | 2018-04-20 | 2021-05-20 | ZF Automotive UK Limited | A radar apparatus for a vehicle and method of detecting misalignment |
CN111142082A (en) * | 2018-11-02 | 2020-05-12 | 现代自动车株式会社 | Apparatus and method for calibrating zero point of radar for vehicle |
US11805390B2 (en) | 2018-12-05 | 2023-10-31 | Here Global B.V. | Method, apparatus, and computer program product for determining sensor orientation |
CN111578839A (en) * | 2020-05-25 | 2020-08-25 | 北京百度网讯科技有限公司 | Obstacle coordinate processing method and device, electronic equipment and readable storage medium |
US20220179431A1 (en) * | 2020-12-04 | 2022-06-09 | Ship And Ocean Industries R&D Center | Assistance system for correcting vessel path and operation method thereof |
US11892854B2 (en) * | 2020-12-04 | 2024-02-06 | Ship And Ocean Industries R&Dcenter | Assistance system for correcting vessel path and operation method thereof |
Also Published As
Publication number | Publication date |
---|---|
US7337650B1 (en) | 2008-03-04 |
US8001860B1 (en) | 2011-08-23 |
US8978439B1 (en) | 2015-03-17 |
US7681448B1 (en) | 2010-03-23 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US8978439B1 (en) | System and apparatus for the alignment of multi-aperture systems | |
US6771208B2 (en) | Multi-sensor system | |
CN106289275B (en) | Unit and method for improving positioning accuracy | |
US8855848B2 (en) | Radar, lidar and camera enhanced methods for vehicle dynamics estimation | |
JP2001227982A (en) | Calibration method for sensor system | |
JP4843190B2 (en) | Image sensor system calibration method and apparatus | |
CN101793528A (en) | Use sensor fusion to estimate the system and method in path, track | |
JP6776202B2 (en) | In-vehicle camera calibration device and method | |
WO2017145541A1 (en) | Mobile object | |
KR20160120467A (en) | Azimuth correction apparatus and method of 2-dimensional radar for vehicle | |
JPWO2018042954A1 (en) | In-vehicle camera, adjustment method of in-vehicle camera, in-vehicle camera system | |
JP2019194559A (en) | Optical atmospheric data compensation system using inertia assistance | |
US11592559B2 (en) | Vehicle sensor fusion | |
RU2733198C1 (en) | Method of vehicle movement control and vehicle movement control device | |
EP3486871B1 (en) | A vision system and method for autonomous driving and/or driver assistance in a motor vehicle | |
KR102038482B1 (en) | Inertial sensor enhancement | |
JP2019212015A (en) | Time synchronization device/method for vehicle data | |
JP5425500B2 (en) | Calibration apparatus and calibration method | |
WO2019239775A1 (en) | Vehicle object sensing device | |
JP6594546B2 (en) | Angle measuring device | |
JP3357290B2 (en) | Vehicle position detection device | |
JP7234840B2 (en) | position estimator | |
JP6901584B2 (en) | Posture sensor device for moving objects | |
JP6704307B2 (en) | Moving amount calculating device and moving amount calculating method | |
JP6632727B2 (en) | Angle measuring device |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: EAGLE HARBOR HOLDINGS, LLC, WASHINGTON Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:PRESTON, DAN ALAN;OLMSTEAD, DAVID;SIGNING DATES FROM 20150213 TO 20150223;REEL/FRAME:035189/0719 |
|
AS | Assignment |
Owner name: NORTHWATER INTELLECTUAL PROPERTY FUND L.P. 2, DELA Free format text: SECURITY INTEREST;ASSIGNOR:EAGLE HARBOR HOLDINGS, LLC;REEL/FRAME:038762/0729 Effective date: 20101115 |
|
AS | Assignment |
Owner name: CLAROVIA TECHNOLOGIES, LLC, WASHINGTON Free format text: SECURITY INTEREST;ASSIGNOR:EAGLE HARBOR HOLDINGS, LLC;REEL/FRAME:041565/0469 Effective date: 20170127 |
|
AS | Assignment |
Owner name: EAGLE HARBOR HOLDINGS, LLC, WASHINGTON Free format text: CORRECTING IMPROPER SECURITY INTEREST;ASSIGNOR:EAGLE HARBOR HOLDINGS, LLC;REEL/FRAME:041651/0884 Effective date: 20170207 |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO PAY ISSUE FEE |