We introduce Collisionpro, a pioneering framework designed to estimate cumulative collision probability distributions using temporal difference learning, specifically tailored to applications in robotics, with a particular em-phasis on autonomous driving. This approach addresses the demand for explainable artificial intelligence (XAI) and seeks to overcome limitations imposed by model-based approaches and conservative constraints. We formulate our framework within the context of reinforcement learning to pave the way for safety-aware agents. Nevertheless, we assert that our approach could prove beneficial in various contexts, including a safety alert system or analytical purposes. A comprehensive examination of our framework is conducted using a realistic autonomous driving simulator, illustrating its high sample efficiency and reliable prediction capabilities for previously unseen collision events. The source code is publicly available.


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    Titel :

    Collision Probability Distribution Estimation via Temporal Difference Learning


    Beteiligte:


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    1050533 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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