IP Library Granted Patent US 11,215,996
Granted Patent B2
US 11,215,996 · App. 16/729,328 · Granted Jan 4, 2022

Method and device for controlling vehicle, device, and storage medium

Inventor: Hao Yu (Beijing, CN)
Assignee: Apollo Intelligent Driving Technology (Beijing) Co., Ltd.
G05D1/0212B60W40/02B60W60/00259B60W2050/0005B60W2420/42
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Quick Facts
Patent No.
US 11,215,996
App. No.
16/729,328
Granted
Jan 4, 2022
Kind
B2
Abstract

The present disclosure provides a method and a device for controlling a vehicle, a device and a storage medium, and relates to the field of unmanned vehicle technologies. The method includes: acquiring a vehicle environment image by an image acquirer during traveling of the vehicle; extracting a static environment image included in the vehicle environment image; obtaining a planned vehicle traveling trajectory by taking the static environment image as an input of a trajectory planning model; and controlling the vehicle to travel according to the planned vehicle traveling trajectory.

Claims (42)

1. A method for controlling a vehicle, comprising:

acquiring a vehicle environment image of the vehicle by an image acquirer during traveling of the vehicle;

extracting a static environment image comprised in the vehicle environment image;

obtaining a planned vehicle traveling trajectory by taking the static environment image as an input of a trajectory planning model, wherein the trajectory planning model is obtained by training a neural network model with taking a historical static environment image as an input of the neural network model, and taking a historical vehicle traveling trajectory associated with the historical static environment image as an output of the neural network model; and

controlling the vehicle to travel according to the planned vehicle traveling trajectory.

2. The method according to claim 1 , wherein extracting the static environment image comprised in the vehicle environment image comprises:

recognizing a dynamic object comprised in the vehicle environment image; and

filtering the dynamic object in the vehicle environment image to obtain the static environment image.

3. The method according to claim 1 , wherein extracting the static environment image comprised in the vehicle environment image comprises:

determining a traveling environment of the vehicle according to the vehicle environment image; and

when the traveling environment is an outdoor traveling environment, extracting a road surface image in the vehicle environment image.

4. The method according to claim 3 , wherein after determining the traveling environment of the vehicle according to the vehicle environment image, the method further comprises:

when the traveling environment is an indoor traveling environment, extracting the road surface image and a ceiling image in the vehicle environment image.

5. A device for controlling a vehicle, comprising:

one or more processors;

a storage device, configured to store one or more programs;

wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to:

acquire a vehicle environment image by an image acquirer during traveling of the vehicle;

extract a static environment image comprised in the vehicle environment image;

obtain a planned vehicle traveling trajectory by taking the static environment image as an input of a trajectory planning model, wherein the trajectory planning model is obtained by training a neural network model with taking a historical static environment image as an input of the neural network model, and taking a historical vehicle traveling trajectory associated with the historical static environment image as an output of the neural network model; and

control the vehicle to travel according to the planned vehicle traveling trajectory.

6. The device according to claim 5 , wherein the one or more processors are configured to:

recognize a dynamic object comprised in the vehicle environment image; and

filter the dynamic object in the vehicle environment image to obtain the static environment image.

7. The device according to claim 5 , wherein the one or more processors are configured to:

determine a traveling environment of the vehicle according to the vehicle environment image; and

extract a road surface image in the vehicle environment image when the traveling environment is an outdoor traveling environment.

8. The device according to claim 7 , wherein the one or more processors are configured to:

extract the road surface image and a ceiling image in the vehicle environment image when the traveling environment is an indoor traveling environment.

9. A non-transitory computer readable storage medium having stored thereon a computer program that, when executed by a processor, causes a method for controlling a vehicle to be implemented, the method comprising:

acquiring a vehicle environment image of the vehicle by an image acquirer during traveling of the vehicle;

extracting a static environment image comprised in the vehicle environment image;

obtaining a planned vehicle traveling trajectory by taking the static environment image as an input of a trajectory planning model, wherein the trajectory planning model is obtained by training a neural network model with taking a historical static environment image as an input of the neural network model, and taking a historical vehicle traveling trajectory associated with the historical static environment image as an output of the neural network model; and

controlling the vehicle to travel according to the planned vehicle traveling trajectory.

10. The non-transitory computer readable storage medium according to claim 9 , wherein extracting the static environment image comprised in the vehicle environment image comprises:

recognizing a dynamic object comprised in the vehicle environment image; and

filtering the dynamic object in the vehicle environment image to obtain the static environment image.

11. The non-transitory computer readable storage medium according to claim 9 , wherein extracting the static environment image comprised in the vehicle environment image comprises:

determining a traveling environment of the vehicle according to the vehicle environment image; and

when the traveling environment is an outdoor traveling environment, extracting a road surface image in the vehicle environment image.

12. The non-transitory computer readable storage medium according to claim 11 , wherein after determining the traveling environment of the vehicle according to the vehicle environment image, the method further comprises:

when the traveling environment is an indoor traveling environment, extracting the road surface image and a ceiling image in the vehicle environment image.

Assignments (3)
CORRECTIVE ASSIGNMENT TO CORRECT THE APPLICANT NAME PREVIOUSLY RECORDED AT REEL: 057933 FRAME: 0812. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Dec 28, 2021
From: BAIDU ONLINE NETWORK TECHNOLOGY (BEIJING) CO., LTD.
To: APOLLO INTELLIGENT DRIVING TECHNOLOGY (BEIJING) CO., LTD.
Reel/Frame 058594/0836 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 13, 2021
From: BAIDU ONLINE NETWORK TECHNOLOGY (BEIJING) CO., LTD.
To: APOLLO INTELLIGENT DRIVING (BEIJING) TECHNOLOGY CO., LTD.
Reel/Frame 057933/0812 →
LABOR CONTRACT Recorded Jan 25, 2021
From: YU, HAO
To: BAIDU ONLINE NETWORK TECHNOLOGY (BEIJING) CO., LTD.
Reel/Frame 055106/0781 →
Priority Claims (1)
CN 201811638546.4 · Dec 29, 2018 · national
Continuity (1)
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