Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver’s behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.


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    Title :

    Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment


    Contributors:


    Publication date :

    2019-10-01


    Size :

    1419009 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English