Intelligent Decision-Making Method for Vehicles in Emergency Conditions Based on Artificial Potential Fields and Finite State Machines-Reference-Cited by-同舟云学术

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Intelligent Decision-Making Method for Vehicles in Emergency Conditions Based on Artificial Potential Fields and Finite State Machines

Published:2024-03 Issue:1 Volume:7 Page:19-29
ISSN:2399-9802
Container-title:Journal of Intelligent and Connected Vehicles
language:
Short-container-title:J. Int. Con. Veh.

Author:

Zheng Xunjia¹,Li Huilan²,Zhang Qiang¹,Liu Yonggang³,Chen Xing⁴,Liu Hui⁴,Luo Tianhong⁴,Gao Jianjie⁵,Xia Lihong¹

Affiliation:

1. China Automotive Engineering Research Institute Co., Ltd.,Chongqing,China,401122

2. Chongqing City Vocational College,Department of Information and Intelligence Engineering,Chongqing,China,402160

3. College of Mechanical and Vehicle Engineering, Chongqing University,Chongqing,China,400044

4. School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences,Chongqing,China,402160

5. Intelligent Policing Key Laboratory of Sichuan Province,Luzhou,China,646000

Funder

National Natural Science Foundation of China

Natural Science Foundation of Chongqing

Publisher

Tsinghua University Press

Link

http://xplorestaging.ieee.org/ielx7/9944931/10520218/10506788.pdf?arnumber=10506788

Reference18 articles.

1. Real-Time Predictive Control of Path Following to Stabilize Autonomous Electric Vehicles Under Extreme Drive Conditions

2. Deep adaptive control: Deep reinforcement learning-based adaptive vehicle trajectory control algorithms for different risk levels

3. Autonomous Car Controller using Behaviour Planning based on Finite State Machine

4. An Optimization-based Path Planning Approach for Autonomous Vehicles using dynEFWA-Artificial Potential Field

5. Path-Following Control of Autonomous Vehicles Considering Coupling Effects and Multi-source System Uncertainties

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