SAH-Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation

Yuqi Fan, Zhiyong Cui, Zhenning Li, Yilong Ren, Haiyang Yu
Beihang University
ICML 2025
Paper Code arXiv

Abstract

Reliable planning is crucial for achieving autonomous driving. Rule-based planners are efficient but lack generalization, while learning-based planners excel in generalization yet have limitations in real-time performance and interpretability. In long-tail scenarios, these challenges make planning particularly difficult. To leverage the strengths of both rule-based and learning-based planners, we proposed the Scenario-Aware Hybrid Planner (SAH-Drive) for closed-loop vehicle trajectory planning. Inspired by human driving behavior, SAH-Drive combines a lightweight rule-based planner and a comprehensive learning-based planner, utilizing a dual-timescale decision neuron to determine the final trajectory. To enhance the computational efficiency and robustness of the hybrid planner, we also employed a diffusion proposal number regulator and a trajectory fusion module. The experimental results show that the proposed method significantly improves the generalization capability of the planning system, achieving state-of-the-art performance in interPlan, while maintaining computational efficiency without incurring substantial additional runtime.

Demo Video

SAH-Drive

PDM-Closed

This video demonstrates the autonomous driving trajectory planning process of SAH-Drive and PDM-Closed,featuring two overtaking scenarios, one accident scenario, and one construction zone scenario within interPlan. SAH-Drive successfully plans trajectories in various long-tail scenarios, whereas PDM-Closed fails to plan due to its inability to perform lane changes.

BibTeX

@misc{fan2025sahdrivescenarioawarehybridplanner,
      title={SAH-Drive: A Scenario-Aware Hybrid Planner for Closed-Loop Vehicle Trajectory Generation}, 
      author={Yuqi Fan and Zhiyong Cui and Zhenning Li and Yilong Ren and Haiyang Yu},
      year={2025},
      eprint={2505.24390},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2505.24390}, 
}