FISS plus : Efficient and Focused Trajectory Generation and Refinement using Fast Iterative Search and Sampling Strategy
2023 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)(2023)
摘要
Trajectory planning plays a crucial role in autonomous driving systems, as it is tasked to generate feasible trajectories under highly dynamic scenarios within the time constraint. This paper proposes a novel two-stage coarse-to-fine framework for efficient sampling-based trajectory planning. The proposed method is designed to iteratively generate new trajectory samples focused on the low-cost regions in the sampling space. Two trajectory exploration algorithms are well-designed for efficient search in discretized coarse global space and continuous fine local space, respectively. Experimental results on the first-of-its-kind planning benchmark tool CommonRoad show that our method significantly outperforms the baseline methods both in optimality and computational efficiency. Overall, our approach offers a promising solution for efficient and effective trajectory planning in more autonomous vehicle applications.
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关键词
Motion and path planning,autonomous vehicle navigation,trajectory sampling
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