Safe and Personalizable Logical Guidance for Trajectory Planning of Autonomous Driving
<|reference_start|>Safe and Personalizable Logical Guidance for Trajectory Planning of Autonomous Driving: Autonomous vehicles necessitate a delicate balance between safety, efficiency, and user preferences in trajectory planning. Existing traditional or learning-based methods face challenges in adequately addressing all these aspects. In response, this paper proposes a novel component termed the Logical Guidance Layer (LGL), designed for seamless integration into autonomous driving trajectory planning frameworks, specifically tailored for highway scenarios. The LGL guides the trajectory planning with a local target area determined through scenario reasoning, scenario evaluation, and guidance area calculation. Integrating the Responsibility-Sensitive Safety (RSS) model, the LGL ensures formal safety guarantees while accommodating various user preferences defined by logical formulae. Experimental validation demonstrates the effectiveness of the LGL in achieving a balance between safety and efficiency, and meeting user preferences in autonomous highway driving scenarios.<|reference_end|>
@article{xu2024safe,
    title={Safe and Personalizable Logical Guidance for Trajectory Planning of
  Autonomous Driving},
    author={Yuejiao Xu, Ruolin Wang, Chengpeng Xu, Jianmin Ji},
    journal={arXiv preprint arXiv:2405.13704},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13704},
    primaryClass={cs.RO cs.LO}
}