IP Library › Granted Patent US 11,529,966
Granted Patent B2
US 11,529,966 · App. 17/220,726 · Granted Dec 20, 2022

Risk prediction on a peer-to-peer network

Inventors: Stephen G. McGill (Cambridge, MA); Guy Rosman (Newton, MA); Luke S. Fletcher (Cambridge, MA)
Assignee: TOYOTA RESEARCH INSTITUTE, INC.
B60W50/14B60W40/04B60W40/09B60W2050/143B60W2420/42
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Quick Facts
Patent No.
US 11,529,966
App. No.
17/220,726
Filed
Apr 1, 2021
Granted
Dec 20, 2022
Kind
B2
Art Unit
2632
USPC
340/436
Abstract

A method for controlling a driving behavior performed by a first agent includes navigating, by the first agent, according to a trajectory and a velocity. The method also includes receiving, from a second agent, a risk identification message identifying a third agent as a potential risk based on the third agent performing a behavior associated with a probability that is less than a threshold. The method further includes autonomously engaging a defensive driving mode in response to receiving the risk identification message. The method still further includes adjusting one or both of the trajectory or the velocity in response to autonomously engaging the defensive driving mode.

Claims (41)

1. A method for controlling a driving behavior performed by a first agent, comprising:

navigating, by the first agent, through an environment according to a trajectory and a velocity;

receiving, from a second agent, a risk identification message indicating a third agent is a potential risk based on the second agent determining the third agent performed behavior at a first time period, the risk identification message being associated with a probability of an observed behavior at a second time period that is after the first time period, the probability being less than a threshold;

autonomously engaging a defensive driving mode in response to receiving the risk identification message; and

adjusting one or both of the trajectory or the velocity in response to autonomously engaging the defensive driving mode.

2. The method of claim 1 , further comprising receiving the risk identification message via at least one of a vehicle-to-vehicle network, a vehicle-to-everything network, a vehicle-to-infrastructure network, a vehicle-to-network network, a vehicle-to-pedestrian network, or a combination thereof.

3. The method of claim 1 , further comprising:

determining at least a portion of a field of view of a sensor of the first agent is blocked; and

transmitting, to the second agent, an occluded view message indicating the first agent's field of view is blocked in response to determining at least the portion of the field of view is blocked.

4. The method of claim 3 , wherein the risk identification message is received based on transmitting the occluded view message.

5. The method of claim 3 , wherein the sensor comprises a red-green-blue camera, a RADAR sensor, or a LiDAR sensor.

6. The method of claim 3 , wherein the sensor fails to identify the third agent prior to the first agent receiving the risk identification message.

7. The method of claim 1 , wherein the probability is based on at least one of environmental conditions, driving regulation, lane geometry, or a combination thereof.

8. An apparatus for controlling a driving behavior of a first agent, comprising:

a processor;

a memory coupled with the processor; and

instructions stored in the memory and operable, when executed by the processor, to cause the apparatus to:

navigate according to a trajectory and a velocity;

receive, from a second agent, a risk identification message indicating a third agent is a potential risk based on the second agent determining the third agent performed a behavior at a first time period, the risk identification message being associated with a probability of an observed behavior at a second time period that is after the first time period, the probability being less than a threshold;

autonomously engage a defensive driving mode in response to the received risk identification message; and

adjust one or both of the trajectory or the velocity in response to the autonomously engaged the defensive driving mode.

9. The apparatus of claim 8 , wherein execution of the instructions further cause the apparatus to receive the risk identification message via at least one of a vehicle-to-vehicle network, a vehicle-to-everything network, a vehicle-to-infrastructure network, a vehicle-to- network network, a vehicle-to-pedestrian network, or a combination thereof.

10. The apparatus of claim 8 , wherein execution of the instructions further cause the apparatus to:

determine at least a portion of a field of view of a sensor of the first agent is blocked; and

transmit, to the second agent, an occluded view message indicating the first agent's field of view is blocked in response in response to determining at least the portion of the field of view is blocked.

11. The apparatus of claim 10 , wherein the risk identification message is received based on transmitting the occluded view message.

12. The apparatus of claim 10 , wherein the sensor comprises a red-green-blue camera, a RADAR sensor, or a LiDAR sensor.

13. The apparatus of claim 10 , wherein the sensor fails to identify the third agent prior to the first agent receiving the risk identification message.

14. The apparatus of claim 8 , wherein the probability is based on and at least one of environmental conditions, driving regulation, lane geometry, or a combination thereof.

15. A non-transitory computer-readable medium having program code recorded thereon for controlling a driving behavior of a first agent, the program code executed by a processor and comprising:

program code to navigate according to a trajectory and a velocity;

program code to receive, from a second agent, a risk identification message indicating a third agent is a potential risk based on the second agent determining the third agent performed a behavior at a first time period, the risk identification message being associated with a probability of an observed behavior at a second time period that is after the first time period, the probability being less than a threshold;

program code to autonomously engage a defensive driving mode in response to the received risk identification message; and

program code to adjust one or both of the trajectory or the velocity in response to the autonomously engaged the defensive driving mode.

16. The non-transitory computer-readable medium of claim 15 , wherein the program code further comprises program code to receive the risk identification message via at least one of a vehicle-to-vehicle network, a vehicle-to-everything network, a vehicle-to-infrastructure network, a vehicle-to-network network, a vehicle-to-pedestrian network, or a combination thereof.

17. The non-transitory computer-readable medium of claim 15 , wherein the program code further comprises:

program code to determine at least a portion of a field of view of a sensor of the first agent is blocked; and

program code to transmit, to the second agent, an occluded view message indicating the first agent's field of view is blocked in response in response to determining at least the portion of the field of view is blocked.

18. The non-transitory computer-readable medium of claim 17 , wherein the risk identification message is received based on transmitting the occluded view message.

19. The non-transitory computer-readable medium of claim 17 , wherein the sensor comprises a red-green-blue camera, a RADAR sensor, or a LiDAR sensor.

20. The non-transitory computer-readable medium of claim 17 , wherein the sensor fails to identify the third agent prior to the first agent receiving the risk identification message.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 9, 2023
From: TOYOTA RESEARCH INSTITUTE, INC.
To: TOYOTA JIDOSHA KABUSHIKI KAISHA
Reel/Frame 062647/0372 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 1, 2021
From: MCGILL, STEPHEN G.; ROSMAN, GUY; FLETCHER, LUKE S.
To: TOYOTA RESEARCH INSTITUTE, INC.
Reel/Frame 056739/0784 →
Continuity (2)
Continuation 16830052 · Mar 25, 2020
Related Publication 20210300400A1 · Sep 30, 2021
Cited By (1)
US 12,725,514