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A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers
Abstract : Lane change is a crucial vehicle maneuver which needs coordination with surrounding vehicles. Automated lane changing functions built on rule-based models may perform well under pre-defined operating conditions, but they may be prone to failure when unexpected situations are encountered. In our study, we propose a Reinforcement Learning based approach to train the vehicle agent to learn an automated lane change behavior such that it can intelligently make a lane change under diverse and even unforeseen scenarios. Particularly, we treat both state space and action space as continuous, and design a unique format of Q-function approximator to estimate the total return which is an accumulated reward over a lane changing process. Extensive simulations are conducted for training the algorithms, and the results illustrate that the Reinforcement Learning based vehicle agent is capable of learning a smooth and efficient driving policy for lane change maneuvers.
Littérature citée [17 références]
https://hal-mines-paristech.archives-ouvertes.fr/hal-01980542
Contributeur : Arnaud de la Fortelle <>
Soumis le : lundi 14 janvier 2019 - 15:00:06
Dernière modification le : vendredi 6 mars 2020 - 01:05:37
Contributeur : Arnaud de la Fortelle <>
Soumis le : lundi 14 janvier 2019 - 15:00:06
Dernière modification le : vendredi 6 mars 2020 - 01:05:37
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Lane change based on Reinforce...
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