FISS+: Efficient and Focused Trajectory Generation and Refinement Using Fast Iterative Search and Sampling Strategy

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.

Authors

• Shuo Sun
• Jie Chen
• Jiawei Sun 0006
• Chengran Yuan
• Yuanchen Li
• Tangyike Zhang
• Marcelo H. Ang

Keywords

• Trajectory planning
• Benchmark testing
• Trajectory
• Computational efficiency
• Space exploration
• Iterative methods
• Time factors
• Iterative Scheme
• Iterative Search
• Fasting Samples
• Fast Strategy
• Trajectory Generation
• Iterative Search Strategy
• Sample Space
• Autonomous Vehicles
• Local Space
• Baseline Methods
• Efficient Planning
• Total Cost
• State Space
• Search Method
• Iterative Refinement
• Number Of Trajectories
• Collision Detection
• Planning Algorithm
• Sampling-based Methods
• Priority Queue
• Exhaustive Search Method
• Planning Cycle
• Search Stage
• Optimization-based Methods
• Cost Term
• Top Element
• Two-stage Framework
• 5th Order
• Motion and path planning
• autonomous vehicle navigation
• trajectory sampling