Safe-State Enhancement Method for Autonomous Driving via Direct Hierarchical Reinforcement Learning
Author:
Affiliation:
1. State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China
2. Alibaba Group, Hangzhou, China
3. Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA, USA
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Alibaba Group through Alibaba Innovative Research Program
Alibaba Research Intern Program and Tsinghua–Toyota Joint Research Fund
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Science Applications,Mechanical Engineering,Automotive Engineering
Link
http://xplorestaging.ieee.org/ielx7/6979/10235283/10120651.pdf?arnumber=10120651
Reference52 articles.
1. Verifiably Safe Off-Model Reinforcement Learning
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3. Model-free safe control for zero-violation reinforcement learning;zhao;Proc Conf Robot Learn,2021
4. Control Barrier Function Based Quadratic Programs for Safety Critical Systems
5. LTP: Lane-based Trajectory Prediction for Autonomous Driving
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