Vehicle control method and apparatus and method and apparatus for acquiring decision-making model

• US 10,429,841 B2
• Filed: 09/22/2016
• Issued: 10/01/2019
• Est. Priority Date: 06/12/2016
• Status: Active Grant

• Current Assignee: Baidu Online Network Technology (Beijing) Co., Ltd (Baidu Incorporated)
• Original Assignee: Baidu Online Network Technology (Beijing) Co., Ltd (Baidu Incorporated)
• Inventors: Yang, Wenli, Zhang, Tianlei, Pan, Yuchang, Sun, Haowen
• Primary Examiner(s): Heinle, Courtney D
• Application Number:
  US15/273,528
• Publication Number:
  US 20170357257A1
• Time in Patent Office: 1,104 Days
• CPC Class Codes: B60W 20/00 Control systems specially adapted for hybrid vehicles hybrid vehicle design, B60K6/00; electric vehicles B60L
• CPC Class Codes: G01C 21/38 Electronic maps specially adapted for navigation; Updating thereof
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: G05B 17/02 electric
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0221 involving a learning process
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 3/042 Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G08G 1/163 involving continuous checking
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

The present invention discloses a vehicle control method and apparatus and a method and apparatus for acquiring a decision-making model. The vehicle control method, comprising: during travel of an unmanned vehicle, acquiring current external environment information and map information in real time; determining vehicle state information corresponding to the external environment information and map information acquired each time according to a decision-making model obtained by pre-training and reflecting correspondence relationship between the external environment information, map information and vehicle state information, and controlling a travel state of the unmanned vehicle according to the determined vehicle state information. Application of the solution of the present invention can improve security and reduce the workload.

Claims

1. A vehicle control method, executed by a computer or processor, comprising:

during travel of an unmanned vehicle, acquiring current external environment information and map information in real time;

determining, by a decision making model, vehicle state information corresponding to the external environment information and map information acquired each time, wherein the decision-making model is obtained by pre-training and reflects correspondence relationship between the external environment information, map information and vehicle state information; and

controlling a travel state of the unmanned vehicle according to the determined vehicle state information;

wherein the external environment information comprises information of surrounding vehicles and information of surrounding pedestrians;

wherein the map information is acquired based on a high-precision map; and

wherein the vehicle state information comprises at least one of the following;

vehicle speed, acceleration, steering wheel angle, angular speed of the steering wheel, a state of a steering indicator light, a state of the high-beam light, and a state of a dipped headlight.

2. The vehicle control method according to claim 1, wherein,

the determining the vehicle state information corresponding to the external environment information and map information acquired each time comprises:

integrating the external environment information and map information acquired each time as input of the decision-making model, and obtaining the vehicle state information output by the decision-making model.

3. The vehicle control method according to claim 1, wherein,

a type of the decision-making model comprises:

Support Vector Machine (SVM) model or neural network model.

4. A vehicle control apparatus, wherein the vehicle control apparatus comprises:

at least one processor; and

a memory storing instructions, which when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising;

during travel of an unmanned vehicle, acquiring current external environment information and map information in real time;

determining, by a decision making model, vehicle state information corresponding to the external environment information and map information acquired each time, wherein the decision-making model is obtained by pre-training and reflects correspondence relationship between the external environment information, map information and vehicle state information; and

controlling a travel state of the unmanned vehicle according to the determined vehicle state information;

wherein the external environment information comprises information of surrounding vehicles and information of surrounding pedestrians;

wherein the map information is acquired based on a high-precision map; and

wherein the vehicle state information comprises at least one of the following;

vehicle speed, acceleration, steering wheel angle, angular speed of the steering wheel, a state of a steering indicator light, a state of the high-beam light, and a state of a dipped headlight.

5. The apparatus according to claim 4, wherein,

the determining the vehicle state information corresponding to the external environment information and map information acquired each time comprises:

integrating the external environment information and map information acquired each time as input of the decision-making model, and obtaining the vehicle state information output by the decision-making model.

6. The apparatus according to claim 4, wherein,

a type of the decision-making model comprises:

Support Vector Machine (SVM) model or neural network model.

7. A non-transitory computer storage medium, wherein the computer storage medium is coded with a computer program, and when the program is executed by one or more computers, the one or more computers perform the following operations:

during travel of an unmanned vehicle, acquiring current external environment information and map information in real time;

determining, by a decision-making model, vehicle state information corresponding to the external environment information and map information acquired each time, wherein the decision-making model is obtained by pre-training and reflects correspondence relationship between the external environment information, map information and vehicle state information; and

controlling a travel state of the unmanned vehicle according to the determined vehicle state information;

wherein the external environment information comprises information of surrounding vehicles and information of surrounding pedestrians;

wherein the map information is acquired based on a high-precision map; and

wherein the vehicle state information comprises at least one of the following;

vehicle speed, acceleration, steering wheel angle, angular speed of the steering wheel, a state of a steering indicator light, a state of the high-beam light, and a state of a dipped headlight.