An important application of intelligent vehicles is advance detection of dangerous events such as collisions. This problem is framed as a problem of optimal alarm choice given predictive models for vehicle location and motion. Techniques for real-time collision detection are surveyed and grouped into three classes: random Monte Carlo sampling, faster deterministic approximations, and machine learning models trained by simulation. Theoretical guarantees on the performance of these collision detection techniques are provided where possible, and empirical analysis is provided for two example scenarios. Results validate Monte Carlo sampling as a robust solution despite its simplicity.


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    Title :

    Optimal alarms for vehicular collision detection


    Contributors:


    Publication date :

    2017-06-01


    Size :

    202525 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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