Navigating urban environments represents a complex task for automated vehicles. They must reach their goal safely and efficiently while considering a multitude of traffic participants. We propose a modular decision making algorithm to autonomously navigate intersections, addressing challenges of existing rule-based and reinforcement learning (RL) approaches. We first present a safe RL algorithm relying on a model-checker to ensure safety guarantees. To make the decision strategy robust to perception errors and occlusions, we introduce a belief update technique using a learning based approach. Finally, we use a scene decomposition approach to scale our algorithm to environments with multiple traffic participants. We empirically demonstrate that our algorithm outperforms rule-based methods and reinforcement learning techniques on a complex intersection scenario.


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    Title :

    Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments


    Contributors:


    Publication date :

    2019-06-01


    Size :

    604364 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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