Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment

2020/07/16 by Teng Liu, Xiaolin Tang, Liu, Teng +9
Engineering · #Artificial Intelligence (cs.AI) #Electric Vehicles and Infrastructure #Electric and Hybrid Vehicle Technologies #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Robotics (cs.RO) #Signal Processing (eess.SP) #Traffic control and management #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2007.09101

openalex publication_date 2020/07/16 · openalex created_date 2020/07/23 · openalex updated_date 2026/07/28

Abstract

As a typical vehicle-cyber-physical-system (V-CPS), connected automated vehicles attracted more and more attention in recent years. This paper focuses on discussing the decision-making (DM) strategy for autonomous vehicles in a connected environment. First, the highway DM problem is formulated, wherein the vehicles can exchange information via wireless networking. Then, two classical reinforcement learning (RL) algorithms, Q-learning and Dyna, are leveraged to derive the DM strategies in a predefined driving scenario. Finally, the control performance of the derived DM policies in safety and efficiency is analyzed. Furthermore, the inherent differences of the RL algorithms are embodied and discussed in DM strategies.

Citations

• Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning

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