Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2007.08691v1
• Date: Thu, 16 Jul 2020 23:41:48 GMT
• Title: Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning
• Authors: Jiangdong Liao, Teng Liu, Xiaolin Tang, Xingyu Mu, Bing Huang, Dongpu Cao
• Abstract summary: A deep reinforcement learning (DRL)-enabled decision-making policy is constructed for autonomous vehicles to address the overtaking behaviors on the highway. A hierarchical control framework is presented to control these vehicles, which indicates the upper-level manages the driving decisions. The DDQN-based overtaking policy could accomplish highway driving tasks efficiently and safely.
• Score: 6.298084785377199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous driving is a promising technology to reduce traffic accidents and improve driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision-making policy is constructed for autonomous vehicles to address the overtaking behaviors on the highway. First, a highway driving environment is founded, wherein the ego vehicle aims to pass through the surrounding vehicles with an efficient and safe maneuver. A hierarchical control framework is presented to control these vehicles, which indicates the upper-level manages the driving decisions, and the lower-level cares about the supervision of vehicle speed and acceleration. Then, the particular DRL method named dueling deep Q-network (DDQN) algorithm is applied to derive the highway decision-making strategy. The exhaustive calculative procedures of deep Q-network and DDQN algorithms are discussed and compared. Finally, a series of estimation simulation experiments are conducted to evaluate the effectiveness of the proposed highway decision-making policy. The advantages of the proposed framework in convergence rate and control performance are illuminated. Simulation results reveal that the DDQN-based overtaking policy could accomplish highway driving tasks efficiently and safely.

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