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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Control Systems

    Background:

    • Autonomous vehicles (AVs) face significant challenges in dynamic and occluded environments, impacting safety and efficiency.
    • Real-time risk assessment and trajectory planning are crucial for AV navigation in complex scenarios.

    Purpose of the Study:

    • To propose an occlusion-aware contingency safety-critical planning approach for real-time autonomous driving.
    • To enhance the safety and travel efficiency of AVs in dynamic, occluded environments.

    Main Methods:

    • Utilized reachability analysis and forward reachable sets (FRSs) of phantom vehicles (PVs) for risk assessment.
    • Developed a biconvex nonlinear programming (NLP) formulation with spatiotemporal barrier constraints for safety enforcement.
    • Employed consensus alternating direction method of multipliers (ADMMs) for real-time computation and decomposition of the NLP problem.

    Main Results:

    • Demonstrated enhanced safety and improved travel efficiency in simulations and real-world experiments.
    • Successfully enabled real-time safe trajectory generation in occluded intersections under varying obstacle conditions.
    • Validated the effectiveness of risk-aware dynamic velocity boundaries derived from FRSs.

    Conclusions:

    • The proposed occlusion-aware planning approach effectively addresses safety and efficiency challenges for AVs in dynamic, occluded environments.
    • The method facilitates real-time, safe trajectory generation, crucial for practical AV deployment.
    • The integration of reachability analysis and ADMM-based optimization offers a robust solution for safety-critical autonomous driving.