Machine learning navigational engine with imposed constraints

Patent metadata

Field	Value
Publication number	US-9977430-B2
Application number	US-201715702753-A
Country	US
Kind code	B2
Filing date	Sep 12, 2017
Priority date	Jan 5, 2016
Publication date	May 22, 2018
Grant date	May 22, 2018

Abstract

Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to: receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; provide the navigational state to a trained navigational system; receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state; analyze the desired navigational action relative to one or more predefined navigational constraints; determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined actual navigational action for the host vehicle.

First claim

What is claimed is:

1. A navigation system for a host vehicle, the system comprising:
at least one processing device programmed to:
receive, from a camera, a plurality of images representative of an environment of the host vehicle;
analyze the plurality of images to identify a navigational state associated with the host vehicle;
provide the navigational state to a trained navigational system;
receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state;
analyze the desired navigational action relative to one or more predefined navigational constraints;
determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints; and
cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined actual navigational action for the host vehicle.

2. The navigation system of claim 1 , wherein the trained navigational network is trained, through reinforcement learning, to account for the predefined navigational constraints in order to provide the desired navigational action.

3. The navigation system of claim 1 , wherein the one or more predefined navigational constraints include a pedestrian envelope.

4. The navigation system of claim 3 , wherein the pedestrian envelope defines a buffer zone, within which navigation of the host vehicle is prohibited, and at least a portion of the buffer zone extends a predetermined distance from a detected pedestrian.

5. The navigation system of claim 4 , wherein the predetermined distance is at least one meter.

6. The navigation system of claim 1 , wherein the one or more predefined navigational constraints include a target vehicle envelope.

7. The navigation system of claim 6 , wherein the target vehicle envelope defines a buffer zone, within which navigation of the host vehicle is prohibited, and at least a portion of the buffer zone extends a predetermined distance from a detected target vehicle.

8. The navigation system of claim 1 , wherein the one or more predefined navigational constraints include a stationary object envelope.

9. The navigation system of claim 8 , wherein the stationary object envelope defines a buffer zone, within which navigation of the host vehicle is prohibited, and at least a portion of the buffer zone extends a predetermined distance from a detected stationary object.

10. The navigation system of claim 9 , wherein the detected stationary object includes at least one of a tree, a pole, a road sign, or an object in a roadway.

11. The navigation system of claim 1 , wherein the one or more predefined navigational constraints include a road barrier envelope.

12. The navigation system of claim 11 , wherein the road barrier envelope defines a buffer zone, within which navigation of the host vehicle is prohibited, and the buffer zone extends a predetermined distance from a surface of a detected road barrier.

13. The navigation system of claim 12 , wherein the road barrier includes a guard rail or a roadside barrier.

14. The navigation system of claim 1 , wherein the one or more predefined navigational constraints vary based on a detected motion associated with an object identified in the plurality of images.

15. The navigation system of claim 1 , wherein the predefined navigational constraint includes a maximum speed of travel when passing within an influence zone of a detected pedestrian.

16. The navigation system of claim 15 , wherein the influence zone is within a predetermined distance of the detected pedestrian.

17. The navigation system of claim 16 , wherein the influence zone is multi-staged, each stage being associated with a different maximum speed of travel.

18. The navigation system of claim 17 , wherein a first stage extends to a distance of at least 10 meters.

19. The navigation system of claim 17 , wherein a second stage extends to a distance of at least 20 meters and is associated with a maximum speed of travel greater than a maximum speed of travel associated with the first stage.

20. The navigation system of claim 1 , wherein the one or more predefined navigational constraints include a maximum deceleration rate.

21. The navigation system of claim 20 , wherein the maximum deceleration rate is determined based on a detected distance to a target vehicle following the host vehicle.

22. The navigation system of claim 1 , wherein the one or more predefined navigational constraints include a mandatory stop at a sensed crosswalk or a railroad crossing.

23. The navigation system of claim 1 , wherein the one or more predefined navigational constraints include a requirement that an exit time of the host vehicle from point at a projected intersection between trajectories of the host vehicle with at least one target vehicle be earlier than an arrival time of the at least one target vehicle.

24. The navigation system of claim 23 , wherein the exit time of the host vehicle and the arrival time of the at least one target vehicle are separated by a predetermined time interval.

25. The navigation system of claim 24 , wherein the predetermined time interval is at least 0.5 seconds.

26. The navigation system of claim 1 , wherein the one or more predefined navigational constraints include a requirement that an arrival time of the host vehicle to a point at a projected intersection between trajectories of the host vehicle with at least one target vehicle be later than an exit time of the at least one target vehicle.

27. The navigation system of claim 26 , wherein the arrival time of the host vehicle and the exit time of the at least one target vehicle are separated by a predetermined time interval.

28. The navigation system of claim 1 , wherein the navigational actuator includes at least one of a steering mechanism, a brake, or an accelerator.

29. An autonomous vehicle, the autonomous vehicle comprising:
a frame;
a body attached to the frame;
a camera; and
a navigation system, the navigation system comprising:
 at least one processing device programmed to:
receive, from the camera, a plurality of images representative of an environment of the vehicle;
analyze the plurality of images to identify a navigational state associated with the vehicle;
provide the navigational state to a trained navigational system;
receive, from the trained navigational system, a desired navigational action for execution by the vehicle in response to the identified navigational state;
analyze the desired navigational action relative to one or more predefined navigational constraints;
determine an actual navigational action for the vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints; and
cause at least one adjustment of a navigational actuator of the vehicle in response to the determined actual navigational action for the host vehicle.

30. A method of navigating an autonomous vehicle, the method comprising:
receiving, from a camera associated with the autonomous vehicle, a plurality of images representative of an environment of the auto

Assignees

• Mobileye Vision Technologies Ltd

Inventors

• Shalev-Shwartz Shai
• Shashua Amnon
• SHAMMAH Shaked

Classifications

• G06N3/006

  based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] · CPC title

• B60W2554/00

  Input parameters relating to objects · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G01C21/3453

  Special cost functions, i.e. other than distance or default speed limit of road segments · CPC title