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Decision-Making System for Lane Change Using Deep Reinforcement Learning in Connected and Automated Driving

Department of Electronics and Computer Engineering, Hanyang University, Seoul 133-791, Korea
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Electronics 2019, 8(5), 543; https://doi.org/10.3390/electronics8050543
Received: 4 April 2019 / Revised: 7 May 2019 / Accepted: 10 May 2019 / Published: 14 May 2019
(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))
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Abstract

Lane changing systems have consistently received attention in the fields of vehicular communication and autonomous vehicles. In this paper, we propose a lane change system that combines deep reinforcement learning and vehicular communication. A host vehicle, trying to change lanes, receives the state information of the host vehicle and a remote vehicle that are both equipped with vehicular communication devices. A deep deterministic policy gradient learning algorithm in the host vehicle determines the high-level action of the host vehicle from the state information. The proposed system learns straight-line driving and collision avoidance actions without vehicle dynamics knowledge. Finally, we consider the update period for the state information from the host and remote vehicles. View Full-Text
Keywords: lane change; decision-making system; vehicular communication; deep reinforcement learning; collision avoidance; connected and automated vehicle lane change; decision-making system; vehicular communication; deep reinforcement learning; collision avoidance; connected and automated vehicle
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An, H.; Jung, J.-I. Decision-Making System for Lane Change Using Deep Reinforcement Learning in Connected and Automated Driving. Electronics 2019, 8, 543.

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