Route Planning for an Autonomous Vehicle

• US 20170356751A1
• Filed: 06/14/2016
• Published: 12/14/2017
• Est. Priority Date: 06/14/2016
• Status: Active Grant

• Current Assignee: Motional AD, LLC (Aptiv PLC)
• Original Assignee: nuTonomy, Inc. (Aptiv PLC)
• Inventors: Iagnemma, Karl
• Granted Patent: US 10,309,792 B2
• Time in Patent Office: Days
• CPC Class Codes: G01C 21/3461 Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
• CPC Class Codes: G01C 21/3691 Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions arrangements for giving variable traffic instructions G08G1/09
• CPC Class Codes: G05D 1/0214 in accordance with safety or protection criteria, e.g. avoiding hazardous areas monitoring the location of vehicles within a certain area, e.g. forbidden or allowed areas, in traffic control systems for road vehicles G08G1/13
• CPC Class Codes: G06F 30/15 Vehicle, aircraft or watercraft design
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005

Abstract

Among other things, a determination is made of and ability of an autonomous vehicle to safely or robustly travel a road feature or a road segment or a route that is being considered for the autonomous vehicle as of a time or range of times. The route conforms to properties of stored road network information. The road feature or road segment or route is eliminated from consideration if the computer has determined that the road feature or road segment or route cannot be safely or robustly traveled by the autonomous vehicle. The determination is based on analysis of performance of the autonomous vehicle.

Claims

1. A computer-implemented method comprising

by computer determining an ability of an autonomous vehicle to travel a road feature or a road segment or a route that is being considered for the autonomous vehicle as of a time or range of times, the route conforming to properties of stored road network information and

eliminating the road feature or road segment or route from consideration based on the computer determining the ability of the vehicle to travel the road feature or road segment or route,

the determination by the computer being based on analysis of performance of the autonomous vehicle, the analysis of performance of the autonomous vehicle comprising analyzing performance of a perception process of the autonomous vehicle.

2. The method of claim 1 in which the analysis of performance of the autonomous vehicle comprises prior driving performance associated with road features.

3. The method of claim 1 in which the analysis of performance of the autonomous vehicle comprises prior simulation performance associated with road features.

4. The method of claim 1, in which the analysis of performance of the autonomous vehicle comprises analyzing dangerous driving performance.

5. The method of claim 1, in which the analysis of performance of the autonomous vehicle comprises analyzing degraded driving performance.

6. The method of claim 1, in which the analysis of performance of the autonomous vehicle comprises analyzing a traffic junction complexity.

7. The method of claim 1, in which the analysis of performance of the autonomous vehicle comprises analyzing a traffic density.

8. The method of claim 1, in which the analysis of performance of the autonomous vehicle comprises analyzing a pedestrian density.

9. The method of claim 1, in which the analysis of performance of the autonomous vehicle comprises analyzing inclement weather.

10. The method of claim 1, in which the analysis of performance of the autonomous vehicle comprises analyzing a slippery road surface.

11. The method of claim 1, in which the analysis of performance of the autonomous vehicle comprises analyzing road features or road segments or routes associated with a failure of the autonomous vehicle.

12. The method of claim 1, in which the analysis of performance of the autonomous vehicle comprises analyzing road features or road segments or routes associated with a false reading of a vehicle sensor.

13. The method of claim 1, in which the analysis of performance of the autonomous vehicle comprises analyzing road features or road segments or routes associated with degraded performance of a vehicle sensor.

14. The method of claim 1, in which the analysis of performance of the autonomous vehicle comprises analyzing road features or road segments associated with a failure of a vehicle sensor.

15. The method of claim 1, in which the analysis of performance of the autonomous vehicle comprises analyzing road features or road segments blocking signal perception of a vehicle sensor.

16. The method of claim 1, in which the analysis of performance of the autonomous vehicle comprises identifying data products that a vehicle sensor cannot generate.

17. The method of claim 1, comprising tagging in a map database the performance of the autonomous vehicle.

18. The method of claim 1, comprising identifying in a map database for avoidance of a road feature or a road segment or a route.

19. The method of claim 18, in which the identifying in a map database for avoidance is based on a measure of poor performance of the autonomous vehicle on the road feature or the road segment or the route, beyond a threshold.

20. the method of claim 1, in which the ability of the autonomous vehicle to travel the road feature or road segment or route comprises the ability of the autonomous vehicle to travel the road feature or road segment or route safely or robustly.

21. The method of claim 1, in which the performance of the perception process comprises reliable detection of signals or signage.

22. The method of claim 1, in which the performance of the perception process comprises occlusion.

23. The method of claim 22, in which the occlusion comprises occlusion from static objects.

24. The method of claim 22, in which the occlusion comprises occlusion from traffic junction complexity.

25. The method of claim 1, in which the performance of the perception process comprises performance of a sensor system.

26. The method of claim 1, in which the performance of the perception process comprises a limited vertical field of view.

27. The method of claim 1, in which the performance of the perception process comprises a limited horizontal field of view.

28. The method of claim 1, in which the performance of the perception process comprises properties of a sensor system.

29. The method of claim 1, in which the performance of the perception process comprises a fault or failure condition.

30. the method of claim 1, in which the performance of the perception process comprises a false sensor reading.