Constraint augmentation in a navigational system

• US 10,649,462 B2
• Filed: 06/27/2018
• Issued: 05/12/2020
• Est. Priority Date: 01/05/2016
• Status: Active Grant

• Current Assignee: MobilEye Vision Technologies Ltd. (Intel Corporation)
• Original Assignee: MobilEye Vision Technologies Ltd. (Intel Corporation)
• Inventors: Shalev-Shwartz, Shai, Shammah, Shaked, Shashua, Amnon, Taieb, Yoav, Stein, Gideon
• Primary Examiner(s): Jeanglaude, Gertrude Arthur
• Application Number:
  US16/019,892
• Publication Number:
  US 20180307239A1
• Time in Patent Office: 685 Days
• Field of Search: 701 23
• CPC Class Codes: B60W 2050/0088 Adaptive recalibration
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2520/16 Pitch
• CPC Class Codes: B60W 2520/18 Roll
• CPC Class Codes: B60W 2552/15 Road slope, i.e. the inclination of a road segment in the longitudinal direction
• CPC Class Codes: B60W 2552/30 Road curve radius
• CPC Class Codes: B60W 2552/53 Road markings, e.g. lane marker or crosswalk
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/20 Static objects
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2554/4046 Behavior, e.g. aggressive or erratic
• CPC Class Codes: B60W 2554/802 Longitudinal distance
• CPC Class Codes: B60W 2554/804 Relative longitudinal speed
• CPC Class Codes: B60W 30/09 Taking automatic action to avoid collision, e.g. braking and steering
• CPC Class Codes: B60W 30/0953 the prediction being responsive to vehicle dynamic parameters
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 30/18163 Lane change; Overtaking manoeuvres
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: B60W 50/045 Monitoring control system parameters
• CPC Class Codes: B60W 60/0011 involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
• CPC Class Codes: B60W 60/00276 : for two or more other traffic participants
• CPC Class Codes: G01C 21/34 : Route searching; Route guidance
• CPC Class Codes: G01C 21/3453 : Special cost functions, i.e. other than distance or default speed limit of road segments
• CPC Class Codes: G01C 21/3602 : Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera
• CPC Class Codes: G05D 1/0055 : with safety arrangements
• CPC Class Codes: G05D 1/0231 : using optical position detecting means position-fixing by using electromagnetic waves other than radio waves, e.g. optical position detecting means G01S5/16
• CPC Class Codes: G05D 1/0246 : using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0253 : extracting relative motion information from a plurality of images taken successively, e.g. visual odometry, optical flow determining position or orientation from images G06T7/70
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 3/00 : Computing arrangements based on biological models
• CPC Class Codes: G06N 3/006 : based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• CPC Class Codes: G06N 3/044 : Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/08 : Learning methods
• CPC Class Codes: G06N 5/046 : Forward inferencing; Production systems
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06V 20/56 : exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/58 : Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
• CPC Class Codes: G06V 20/584 : of vehicle lights or traffic lights
• CPC Class Codes: G06V 20/588 : Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road

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.

Claims

1. A navigation system for a host vehicle, the system comprising:

at least one processing device programmed to;

identify a navigational state associated with the host vehicle;

determine a first predefined navigational constraint implicated by at least one aspect of the navigational state;

identify a presence of at least one navigational constraint augmentation factor;

determine a second navigational constraint based on identification of the at least one navigational constraint augmentation factor, wherein the second navigational constraint is different from the first navigational constraint and includes at least one characteristic augmented with respect to the first navigational constraint;

determine, based on the identified navigational state, a navigational action for the host vehicle satisfying the second navigational constraint; and

cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined navigational action.

2. The navigation system of claim 1, wherein determination of the navigational action for the host vehicle satisfying the second navigational constraint occurs in a trained navigational system.

3. The navigation system of claim 1, wherein the first predefined navigational constraint includes a first buffer zone associated with a detected object, wherein at least a portion of the first buffer zone extends a first distance from the detected object, and wherein the second navigational constraint includes a second buffer zone associated with the detected object, wherein at least a portion of the second buffer zone extends a second distance from the detected object greater than the first distance.

4. The navigation system of claim 1, wherein the at least one navigational constraint augmentation factor includes a detected presence of snow or water on a road surface.

5. The navigation system of claim 1, wherein the at least one navigational constraint augmentation factor includes a detected decrease in image quality of a plurality of images representative of an environment of the host vehicle.

6. The navigation system of claim 1, wherein the at least one navigational constraint augmentation factor includes detected particulates on an outer surface of a windshield of the host vehicle.

7. The navigation system of claim 1, wherein the at least one navigational constraint augmentation factor includes a detected failure of a least one sensor associated with the host vehicle.

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

9. The navigation system of claim 1, wherein that least one augmented characteristic includes an increased distance of at least a portion of a buffer zone associated with at least one of a pedestrian, a target vehicle, a detected object, or a roadside barrier.

10. The navigation system of claim 1, wherein the at least one augmented characteristic includes a decrease in speed associated with at least one predefined navigational constraint.

11. The navigation system of claim 1, wherein the at least one augmented characteristic includes a decrease in maximum allowable deceleration associated with at least one predefined navigational constraint.

12. An autonomous vehicle, the autonomous vehicle comprising:

a frame;

a body attached to the frame; and

at least one processing device programmed to;

identify a navigational state associated with the autonomous vehicle;

determine a first predefined navigational constraint implicated by at least one aspect of the navigational state;

identify a presence of at least one navigational constraint augmentation factor;

determine a second navigational constraint based on identification of the at least one navigational constraint augmentation factor, wherein the second navigational constraint is different from the first navigational constraint and includes at least one characteristic augmented with respect to the first navigational constraint;

determine, based on the identified navigational state, a navigational action for the autonomous vehicle satisfying the second navigational constraint; and

cause at least one adjustment of a navigational actuator of the autonomous vehicle in response to the determined navigational action.

13. The autonomous vehicle of claim 12, wherein determination of the navigational action for the autonomous vehicle satisfying the second navigational constraint occurs in a trained navigational system.

14. The autonomous vehicle of claim 12, wherein the first predefined navigational constraint includes a first buffer zone associated with a detected object, wherein at least a portion of the first buffer zone extends a first distance from the detected object, and wherein the second navigational constraint includes a second buffer zone associated with the detected object, wherein at least a portion of the second buffer zone extends a second distance from the detected object greater than the first distance.

15. The autonomous vehicle of claim 12, wherein the at least one navigational constraint augmentation factor includes a detected presence of snow or water on a road surface.

16. The autonomous vehicle of claim 12, wherein the at least one navigational constraint augmentation factor includes a detected decrease in image quality of a plurality of images representative of an environment of the host vehicle.

17. The autonomous vehicle of claim 12, wherein the at least one navigational constraint augmentation factor includes detected particulates on an outer surface of a windshield of the autonomous vehicle.

18. The autonomous vehicle of claim 12, wherein the at least one navigational constraint augmentation factor includes a detected failure of a least one sensor associated with the autonomous vehicle.

19. The autonomous vehicle of claim 12, wherein the navigational actuator includes at least one of a steering mechanism, a brake, or an accelerator.

20. The autonomous vehicle of claim 12, wherein that least one augmented characteristic includes an increased distance of at least a portion of a buffer zone associated with at least one of a pedestrian, a target vehicle, a detected object, or a roadside barrier.

21. The autonomous vehicle of claim 12, wherein the at least one augmented characteristic includes a decrease in speed associated with at least one predefined navigational constraint.

22. The autonomous vehicle of claim 12, wherein the at least one augmented characteristic includes a decrease in maximum allowable deceleration associated with at least one predefined navigational constraint.

23. A method of navigating an autonomous vehicle, the method comprising:

identifying a navigational state associated with the autonomous vehicle;

determining a first predefined navigational constraint implicated by at least one aspect of the navigational state;

identifying a presence of at least one navigational constraint augmentation factor;

determining a second navigational constraint based on identification of the at least one navigational constraint augmentation factor, wherein the second navigational constraint is different from the first navigational constraint and includes at least one characteristic augmented with respect to the first navigational constraint;

determining, based on the identified navigational state, a navigational action for the autonomous vehicle satisfying the second navigational constraint; and

causing at least one adjustment of a navigational actuator of the autonomous vehicle in response to the determined navigational action.