FISS+: Efficient and Focused Trajectory Generation and Refinement Using Fast Iterative Search and Sampling Strategy
Shuo Sun, Jie Chen, Jiawei Sun, Chengran Yuan, Yuanchen Li, Tangyike Zhang, Marcelo H. Ang
Abstract
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.
BibTeX
@inproceedings{iros2023_fissefficientand,
title = {FISS+: Efficient and Focused Trajectory Generation and Refinement Using Fast Iterative Search and Sampling Strategy},
author = {Shuo Sun and Jie Chen and Jiawei Sun and Chengran Yuan and Yuanchen Li and Tangyike Zhang and Marcelo H. Ang},
booktitle = {IROS 2023},
year = {2023}
}