Barrier-Enhanced Homotopic Parallel Trajectory Optimization for Safety-Critical Autonomous Driving
<|reference_start|>Barrier-Enhanced Homotopic Parallel Trajectory Optimization for Safety-Critical Autonomous Driving: Enforcing safety while preventing overly conservative behaviors is essential for autonomous vehicles to achieve high task performance. In this paper, we propose a barrier-enhanced homotopic parallel trajectory optimization (BHPTO) approach with over-relaxed alternating direction method of multipliers (ADMM) for real-time integrated decision-making and planning. To facilitate safety interactions between the ego vehicle (EV) and surrounding vehicles, a spatiotemporal safety module exhibiting bi-convexity is developed on the basis of barrier function. Varying barrier coefficients are adopted for different time steps in a planning horizon to account for the motion uncertainties of surrounding HVs and mitigate conservative behaviors. Additionally, we exploit the discrete characteristics of driving maneuvers to initialize nominal behavior-oriented free-end homotopic trajectories based on reachability analysis, and each trajectory is locally constrained to a specific driving maneuver while sharing the same task objectives. By leveraging the bi-convexity of the safety module and the kinematics of the EV, we formulate the BHPTO as a bi-convex optimization problem. Then constraint transcription and over-relaxed ADMM are employed to streamline the optimization process, such that multiple trajectories are generated in real time with feasibility guarantees. Through a series of experiments, the proposed development demonstrates improved task accuracy, stability, and consistency in various traffic scenarios using synthetic and real-world traffic datasets.<|reference_end|>
@article{zheng2024barrier-enhanced,
    title={Barrier-Enhanced Homotopic Parallel Trajectory Optimization for
  Safety-Critical Autonomous Driving},
    author={Lei Zheng, Rui Yang, Michael Yu Wang, Jun Ma},
    journal={arXiv preprint arXiv:2402.10441},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10441},
    primaryClass={cs.RO}
}