Hierarchical Trajectory Planning Based on Adaptive Motion Primitives and Bilevel Corridor

Shihao Li, Wenshuo Wang, Boyang Wang*, Haijie Guan, Haiou Liu, Shaobin Wu, Huiyan Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents an efficient and risk-aware search-optimization hierarchical trajectory planning method for automated vehicles in different road structures. The proposed approach incorporates a time-series motion risk field, capturing diverse road structures through a spatiotemporal map. Then, an adaptive motion primitive is developed, dynamically adjusting action time windows based on evolving risk and expected deviation during the search process. This enables efficient and accurate initial trajectory generation. Additionally, a bilevel corridor is introduced to extract the drivable area and re-represent the risk field, enabling trajectory smoothing to consider motion risk without resorting to non-convex optimization methods. Simulation results in structured and unstructured scenarios demonstrate that the proposed method improves efficiency, flexibility, and optimization quality compared to fixed-step search and single-level corridor-based optimization approaches. Real-world experiments on autonomous vehicles validated the dynamic characteristics and effectiveness of the proposed method in the actual environment.

Original languageEnglish
Pages (from-to)16238-16253
Number of pages16
JournalIEEE Transactions on Vehicular Technology
Volume73
Issue number11
DOIs
Publication statusPublished - 2024

Keywords

  • Trajectory planning
  • corridor-based optimization
  • motion primitives
  • motion risk

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