GPS/IMU/Video/Radar absolute/relative positioning communication/computation sensor platform for automotive safety applications

• US 20120290146A1
• Filed: 07/15/2011
• Published: 11/15/2012
• Est. Priority Date: 07/15/2010
• Status: Active Grant

• Current Assignee: Uatc, LLC (Uber Technologies, Inc.)
• Original Assignee: George C. Dedes, Kyriakos C. Mouskos
• Inventors: Dedes, George C., Mouskos, Kyriakos C.
• Granted Patent: US 8,639,426 B2
• Time in Patent Office: Days
• US Class Current: 701/1
• CPC Class Codes: B60T 2201/08 Lane monitoring; Lane Keeping Systems
• CPC Class Codes: B60T 2210/36 Global Positioning System [GPS]
• CPC Class Codes: G01C 21/165 combined with non-inertial navigation instruments
• CPC Class Codes: G01C 21/3697 Output of additional, non-guidance related information, e.g. low fuel level G01C21/3679 takes precedence
• CPC Class Codes: G08G 1/163 involving continuous checking
• CPC Class Codes: G08G 1/164 Centralised systems, e.g. external to vehicles

Abstract

A GPS/IMU safety sensor platform is proposed, consisting of data fusion Processors, GNSS Signal Acquisition and Tracking Processors, MEMS IMU sensors, one or multiple accelerometers able to provide orientation information, optional V2V communication modules, and optional V2I communication modules. The data fusion processors provide interface ports to GNSS/IMU processors, odometers, video (Visual/Infrared) cameras installed in the vehicle, V2V relative positioning sensors (laser, radar or any other distance measuring), and V2V and V2I communication modules. The data fusion processors are interfaced to a driver warning system and optionally to the vehicle controls for providing safety warning messages to drivers, or for automatic control the vehicle for preventing and reducing accidents.

Claims

1. ) The architecture of a device platform specifically designed for automotive safety applications. The device platform architecture consists of a central data fusing processor, with communication paths to Vehicle-2-Vehicle (V2V) communication cards, to Vehicle-2-Infrastructure communication cards, to the host vehicle controls, to a GNSS signal tracking and acquisition device, to an Inertial Measurement Unit (IMU) sensor, to accelerometers distributed along the body of the host vehicle, to radars, to video cameras and to any other sensors installed in the host vehicle which can provide data able to enhance the host vehicle position and orientation estimation (e.g. compasses, inclinometers, etc.).

2. ) The device architecture platform of claim 1, wherein at least one or both of the communication paths exists to Vehicle-2-Vehicle (V2V) communication cards, to Vehicle-2-Infrastructure communication cards, to the host vehicle controls, to a GNSS signal tracking and acquisition device, to an Inertial Measurement Unit (IMU) sensor, to accelerometers distributed along the body of the host vehicle, to radars, to video cameras and to any other sensor installed in the host vehicle which can provide data able to enhance the host vehicle position and orientation estimation (e.g. compasses, inclinometers, odometers, etc.)

3. ) The device architecture platform of claim 1, wherein the V2V and V2I communication paths facilitate the reception of GNSS carrier phase and pseudo range measurements as well orientation and acceleration information from the other vehicles in the network.

4. ) The device of platform of claim 1, wherein the V2V and V2I communication paths facilitate the reception of measurements from any positioning sensors (e.g., radars, cameras, compasses, inclinometers, etc.) installed in the other cars in the network.

5. ) The device architecture platform of claim 1, wherein the IMU sensor is replaced with accelerometers distributed over the body of the car.

6. ) The device architecture of claim 1, wherein the central fusing processor has communication paths to the vehicle controls to issue automated commands for avoiding impeding accidents.

7. ) The device architecture of claim 1, wherein the central data fusing processor has communication paths to the host vehicle audio and display devices to issue warning messages.

8. ) The device architecture of claim 1, wherein the central data fusing processor has communication paths to the V2V and V2I communication cards to issue warning to the other vehicles in the network.

9. ) The device architecture of claim 1, where in the central fusing processor has communication paths to a permanent storage device to store the host vehicle and the other vehicle positioning measurement information for post-mortem analysis.

10. ) The device architecture of claim 1, wherein the central data fusing processor consists of any combination of multi/Single-core of GPPs, DSPs, FPGAS, CPUs, or other ICs for specific product line applications.

11. ) The device architecture of claim 1, wherein the central data fusing processor resides inside a remote server located within the Internet cloud infrastructure.

12. ) The device architecture of claim 1, wherein the GNSS Signal/Tracking and acquisition processor can be any combination of GPPs, DSPs, CPUs, FPGAs, ASICs, or ICs specifically designed for GNSS acquisition and tracking.

13. ) The data fusion inside the central processor of claims 1 and 11 for the optimum estimation of the position and orientation (pitch, yaw, and roll) of the host vehicle and its relative position and orientation (pitch, yaw and roll) with respect to the rest of the vehicles in the network.

14. ) The data fusion inside the central processor of claims 1 and 13, wherein the host vehicle GNSS measurements (carrier phases, pseudo-ranges) and/or host vehicle Inertial Measurement Unit (INU) measurements and/or host vehicle accelerometer measurements (accelerations) and/or host vehicle radar measurements (relative distance and speed to other cars) and/or host vehicle video cameras (images with relative geometry to other vehicles extracted) and/or host vehicle accelerometer measurements (accelerations) and/or host vehicle compass measurements (magnetic azimuth) and/or host vehicle inclinometer measurements (pitch) and/or any other host vehicle sensor measurements are optimally combined to improve the position and orientation (pitch, yaw and roll) estimation of the host vehicle.

15. ) The data fusion inside the central processor of claims 1, 13 and 14, wherein GNSS measurements (carrier phases and pseudo ranges) from other vehicles in the network are fused (optimally combined) with the corresponding measurements of the host vehicle to estimate the relative position of the host vehicle with respect to the other vehicles in the network.

16. ) The data fusion inside the central processor of claims 1, 13, 14, and 15, wherein other vehicle Inertial Measurement Unit (IMU) measurements (relative orientation and accelerations) and/or other vehicle radar measurements (relative distance and speed) and/or other vehicle image measurements (relative distance and speed) and/or other vehicle compass measurements (magnetic north) and/or other vehicle inclinometer measurements (pitch) and/or other vehicle sensor measurements are optimally combined to compute the relative position and relative orientation (pitch, yaw and roll) of the host vehicle with respect to the other vehicles in the network.

17. ) The data fusion inside the central processor of claims 1, 13, 14, 15, and 16 wherein the position and orientation (pitch, yaw and roll) of the host vehicle combined with the vehicle dynamics theory for different types of vehicles (compact, economy, SUVs, trucks etc) are used to estimate impeding accidents (road departure, collision with the road infrastructure) and issue warnings to the driver or automatically invoke the host vehicle controls in order to avoid accidents.

18. ) The data fusion inside the central processor of claims 1, 13, 14, 15, and 16 wherein the relative position, speed and orientation of the host vehicle with respect to the other vehicles in the network combined with vehicle dynamics theory for different type of vehicles (compact, economy, SUVs, trucks etc) are used to compute the real-time trajectories (position, speed, orientation (pitch/yaw/roll)) of the host vehicle and the other vehicles in the network and predict impeding accidents (rear-end collision, side-collision, head-collision, intersection collision etc).

19. ) The data fusion inside the central processor of claims 1, 13, 14, 15, 16, 17, and 18 wherein the prediction of impeding accidents are used to issue warning messages to the driver and issue commands automatically to the vehicle controls to avoid impeding accidents for which the driver has not enough time to respond.