Inverse Reinforcement Learning Based: Segmented Lane-Change Trajectory Planning With Consideration of Interactive Driving Intention

Author:

Sun Yingbo1ORCID,Chu Yuan1,Xu Tao1ORCID,Li Jingyuan1,Ji Xuewu1

Affiliation:

1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Cited by 10 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. Automatic Parameter Tuning of Self-Driving Vehicles;2024 IEEE Conference on Control Technology and Applications (CCTA);2024-08-21

2. Improved time series models for the prediction of lane-change intention;Transportation Letters;2024-07-22

3. Driving Style-aware Car-following Considering Cut-in Tendencies of Adjacent Vehicles with Inverse Reinforcement Learning;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

4. Empirical study of deep reinforcement learning algorithms for CartPole problem;2024 11th International Conference on Signal Processing and Integrated Networks (SPIN);2024-03-21

5. Enhancing Safety and Efficiency in Automated Container Terminals: Route Planning for Hazardous Material AGV Using LSTM Neural Network and Deep Q-Network;Journal of Intelligent and Connected Vehicles;2024-03

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