Hierarchical motion planning system with consideration of the dynamic lane-changing behaviour
Author:
Affiliation:
1. School of Mechanical Engineering, Southeast University,Nanjing,China,211189
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9921415/9921739/09922601.pdf?arnumber=9922601
Reference19 articles.
1. Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles
2. If, When, and How to Perform Lane Change Maneuvers on Highways
3. Toward Safe and Personalized Autonomous Driving: Decision-Making and Motion Control With DPF and CDT Techniques
4. On-ramp traffic merging using cooperative intelligent vehicles: A slot-based approach
5. Dynamic Trajectory Planning of Autonomous Lane Change at Medium and Low Speeds Based on Elastic Soft Constraint of the Safety Domain
Cited by 4 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. Enhancing Autonomous Lane-Changing Safety: Deep Reinforcement Learning via Pre-Exploration in Parallel Imaginary Environments;IEEE Transactions on Industrial Informatics;2024
2. Pedestrian Environment Model for Automated Driving;2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC);2023-09-24
3. Time-to-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles;2023 IEEE Intelligent Vehicles Symposium (IV);2023-06-04
4. Trajectory Planning of Automated Vehicles Using Real-Time Map Updates;IEEE Access;2023
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