期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 7, 期 4, 页码 11839-11846出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2022.3207555
关键词
Autonomous vehicle navigation; integrated planning and learning; motion and path planning
类别
资金
- Guangdong Basic and Applied Basic Research Foundation [2021B1515120032]
- Zhongshan Science and Technology Bureau Fund [2020AG002]
- Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone [HZQB-KCZYB-2020083]
This paper proposes a learning-based Interaction Point Model (IPM) to describe the interaction between agents in autonomous driving, and integrates the model into a planning framework, demonstrating its effectiveness and robustness through comprehensive simulations in highly dynamic environments.
Safely interacting with other traffic participants is one of the core requirements for autonomous driving, especially in intersections and occlusions. Most existing approaches are designed for particular scenarios and require significant human labor in parameter tuning to be applied to different situations. To solve this problem, we first propose a learning-based Interaction Point Model (IPM), which describes the interaction between agents with the protection time and interaction priority in a unified manner. We further integrate the proposed IPM into a novel planning framework, demonstrating its effectiveness and robustness through comprehensive simulations in highly dynamic environments.
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