Counterfactual Evolutionary Reasoning for Virtual Driver Reinforcement Learning in Safe Driving
Author:
Affiliation:
1. State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2. School of Rail Transportation, Shandong Jiaotong University, Jinan, China
Funder
National Natural Science Foundation of China
Youth Innovation Promotion Association of the Chinese Academy of Sciences
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Artificial Intelligence,Control and Optimization,Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/7274857/10385201/10274149.pdf?arnumber=10274149
Reference40 articles.
1. Fail-Safe Behavior and Motion Planning Incorporating Shared Control for Potential Driver Intervention
2. Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
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4. Rule-based optimal control for autonomous driving
5. End-to-End Driving Via Conditional Imitation Learning
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