FISS: A Trajectory Planning Framework Using Fast Iterative Search and Sampling Strategy for Autonomous Driving

Abstract

Trajectory planning is a critical component in autonomous vehicles directly responsible for driving safety and efficiency during deployment. The ability to find the optimal trajectory in real-time is critical for autonomous driving. This paper presents a novel general framework using the Fast Iterative Search and Sampling (FISS) strategy for sampling-based trajectory planning, which can find the optimal trajectory from an enormous number of candidates with high efficiency in real-time. Specifically, before generating any trajectories, the proposed method utilizes historical planning results as prior information in heuristics to estimate the cost distribution over the sampling space. On this basis, the Fast Iterative Search and Sampling strategy is employed to explore the sampling space for possible candidates and generate trajectories for verification during the search process. Experimental results show that our method can significantly outperform existing frameworks by order of magnitude in planning efficiency while ensuring safety and maintaining high accuracy.