Decision-making system for lane change using deep reinforcement learning in connected and automated driving
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HongIl An [1] ; Jae-il Jung [1]
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[1] Hanyang University
Hanyang University
Corea del Sur
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- Localización: Intelligent Transportation Systems (ITS) / coord. por Beatriz López Boada, 2021, ISBN 978-3-0365-0506-0, págs. 219-232
- Idioma: inglés
- Enlaces
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Resumen
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 deepreinforcement 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.
