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    This study introduces optimization embedded reinforcement learning (OERL) for adaptive automated vehicle decisions at roundabouts. The OERL method enhances safety and efficiency by enabling real-time adjustments to driving behaviors.

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    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Autonomous Driving Systems

    Background:

    • Roundabouts present complex, dynamic environments requiring adaptive decision-making for automated vehicles.
    • Existing methods may lack the adaptability needed for the continuous and interactive nature of roundabout scenarios.

    Purpose of the Study:

    • To develop an adaptive decision-making framework for automated vehicles navigating roundabouts.
    • To enhance the safety and efficiency of automated driving in complex intersection scenarios.

    Main Methods:

    • Proposed an optimization embedded reinforcement learning (OERL) approach, modifying the Actor-Critic framework.
    • Integrated model-based optimization within reinforcement learning for direct exploration of continuous action spaces.
    • Designed task representation to restructure the policy network for adaptability to diverse roundabout types.

    Main Results:

    • The OERL method achieved high sample efficiency, determining macroscale (lane change) and medium-scale (acceleration, action time) behaviors simultaneously.
    • Demonstrated timely adjustment of medium-scale behaviors via direct search, enhancing decision-making adaptability.
    • Simulations confirmed the method's ability to adapt decisions to untrained roundabout types and hazardous situations, outperforming baseline methods.

    Conclusions:

    • The proposed OERL framework provides an efficient and adaptive solution for automated vehicle decision-making in roundabouts.
    • The approach successfully mimics human driving behaviors while improving system performance and safety.
    • OERL shows significant potential for advancing autonomous driving capabilities in complex, real-world scenarios.