Decision-making in automated driving is influenced both by objective traffic rules and subjective perceptions and goals of the driver. Thus, a suitable representation of the environment of the autonomous vehicle is required to model complex traffic situations and extract key features. To achieve this objective, this work uses an ontology-based situation interpretation (OBSI) to model traffic situations. The resulting semantic state representation is used to train models of vehicle-controlling agents using reinforcement learning. Based on our simulations, it can be shown that the semantic preprocessing of traffic situations significantly improves the agent's performance regarding safety and driving style.


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    Title :

    A Hybrid Tactical Decision-Making Approach in Automated Driving Combining Knowledge-Based Systems and Reinforcement Learning


    Contributors:


    Publication date :

    2022-10-08


    Size :

    687435 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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