A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning-Reference-Cited by-同舟云学术

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A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning

Published:2021-05-30 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International Conference on Robotics and Automation (ICRA)
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Author:

Li Jinning,Sun Liting,Chen Jianyu,Tomizuka Masayoshi,Zhan Wei

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9560720/9560666/09561195.pdf?arnumber=9561195

Cited by 23 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey;Transportation Research Part C: Emerging Technologies;2024-07

2. Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. Research on lane-changing decision model with driving style based on XGBoost;Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023);2024-04-09

4. How to Guarantee Driving Safety for Autonomous Vehicles in a Real-World Environment: A Perspective on Self-Evolution Mechanisms;IEEE Intelligent Transportation Systems Magazine;2024-03

5. A Comprehensive Review on Deep Learning-Based Motion Planning and End-to-End Learning for Self-Driving Vehicle;IEEE Access;2024

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