Systems and methods for obstacle avoidance and path planning in autonomous vehicles

• US 10,317,907 B2
• Filed: 04/28/2017
• Issued: 06/11/2019
• Est. Priority Date: 04/28/2017
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: Shami, Constandi J.
• Primary Examiner(s): Shaawat, Mussa A
• Application Number:
  US15/581,467
• Publication Number:
  US 20180314259A1
• Time in Patent Office: 774 Days
• CPC Class Codes: B60W 2050/143 Alarm means
• CPC Class Codes: B60W 2050/146 Display means
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• 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 50/14 Means for informing the driver, warning the driver or prompting a driver intervention
• 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: G05D 1/0238 using obstacle or wall sensors G05D1/0246 and G05D1/0289 take precedence; lidar systems designed for anti-collision purposes G01S17/93
• CPC Class Codes: G05D 1/0276 using signals provided by a source external to the vehicle involving a plurality of vehicles G05D1/0287; automatically controlling vehicle speed responsive to externally generated signals B60K31/0058
• CPC Class Codes: G05D 1/0287 involving a plurality of land vehicles, e.g. fleet or convoy travelling traffic control systems for road vehicles G08G1/00, particularly anticollision systems G08G1/16
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0145 for active traffic flow control
• CPC Class Codes: G08G 1/165 for passive traffic, e.g. including static obstacles, trees

Abstract

Systems and method are provided for controlling a vehicle. A path planning method for a vehicle includes establishing a first path for a first vehicle, then receiving, at the first vehicle, sensor data associated with a behavior of one or more leading vehicles observed in the vicinity of the first vehicle. The method further includes establishing a modified path that diverges from the first path based on the sensor data, then classifying, with a processor, an obstacle observed by the first vehicle as it travels along the modified path.

Claims

1. A path planning method for a vehicle, the method comprising:

establishing a first path for a first vehicle;

receiving, at the first vehicle, sensor data associated with a path of one or more leading vehicles observed in the vicinity of the first vehicle;

establishing a modified path that diverges from the first path a and that follows a path of the one or more leading vehicles;

detecting, at the first vehicle, an obstacle in a vicinity to the first path while the first vehicle is traveling along the modified path; and

classifying, with a processor, the obstacle observed by the first vehicle along the modified path.

2. The method of claim 1, wherein the obstacle is classified as one of a transient obstacle and an embedded obstacle.

3. The method of claim 1, further including documenting a plurality of characteristics of the observed obstacle and transmitting the characteristics to a remote database via a network.

4. The method of claim 3, wherein the plurality of characteristics includes a severity measure associated with the observed obstacle.

5. The method of claim 3, wherein the plurality of characteristics includes an estimate of the likely duration of the observed obstacle'"'"'s presence.

6. The method of claim 3, wherein the plurality of characteristics includes an indication of lane placement of the observed obstacle.

7. The method of claim 3, further including modifying a map database based on the plurality of characteristics stored in the remote database.

8. An autonomous vehicle comprising:

one or more sensors configured to produce sensor data associated one or more leading vehicles located in the vicinity of the autonomous vehicle;

a leading vehicle observation module configured to, by a processor, receive the sensor data and establish a modified path that diverges from an initially established path and that follows a path of the one or more leading vehicles;

an obstacle classification module, including a processor, configured to detect an obstacle in a vicinity to the first path while the first vehicle is traveling along the modified path; and

classify the obstacle observed by the autonomous vehicle along the modified path.

9. The autonomous vehicle of claim 8, wherein the obstacle classification module classifies the obstacle as one of a transient obstacle and an embedded obstacle.

10. The autonomous vehicle of claim 8, further including documenting a plurality of characteristics of the observed obstacle and transmitting the characteristics to a remote database via a network.

11. The autonomous vehicle of claim 10, wherein the plurality of characteristics includes a severity measure associated with the observed obstacle.

12. The autonomous vehicle of claim 10, wherein the plurality of characteristics includes an estimate of the likely duration of the observed obstacle'"'"'s presence.

13. The autonomous vehicle of claim 10, wherein the plurality of characteristics includes an indication of lane placement of the observed obstacle.

14. The autonomous vehicle of claim 8, further including modifying a map database based on the plurality of characteristics stored in the remote database.

15. A system for controlling a vehicle, comprising:

one or more sensors configured to produce sensor data associated one or more leading vehicles located in the vicinity of the autonomous vehicle;

a leading vehicle observation module, including a processor, configured to receive the sensor data and establish a modified path that diverges from an initially established path and that follows a path of the one or more leading vehicles; and

an obstacle classification module configured to detect an obstacle in a vicinity to the first path while the first vehicle is traveling along the modified path; and

classify the obstacle observed by the autonomous vehicle along the modified path.

16. The system of claim 15, wherein the obstacle classification module classifies the obstacle as one of a transient obstacle and an embedded obstacle.

17. The system of claim 15, wherein the obstacle classification module is further configured to document a plurality of characteristics of the observed obstacle and transmit the characteristics to a remote database via a network.

18. The system of claim 17, wherein the plurality of characteristics includes a severity measure associated with the observed obstacle.

19. The system of claim 18, wherein the severity measure is determined via an inertial measurement unit provided within the vehicle.

20. The system of claim 17, wherein the plurality of characteristics includes an estimate of the likely duration of the observed obstacle'"'"'s presence.