Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study

• URL: http://arxiv.org/abs/2007.08343v1
• Date: Thu, 16 Jul 2020 14:09:20 GMT
• Title: Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study
• Authors: Teng Liu, Xingyu Mu, Xiaolin Tang, Bing Huang, Hong Wang, Dongpu Cao
• Abstract summary: This work optimize the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL) The overtaking decision-making problem of the automated vehicle is formulated as an optimal control problem. Simulation results reveal that the ego vehicle could safely and efficiently accomplish the driving task after learning and training.
• Score: 9.602219035367066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work optimizes the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL). First, the highway driving environment is built, wherein the ego vehicle, surrounding vehicles, and road lanes are included. Then, the overtaking decision-making problem of the automated vehicle is formulated as an optimal control problem. Then relevant control actions, state variables, and optimization objectives are elaborated. Finally, the deep Q-network is applied to derive the intelligent driving policies for the ego vehicle. Simulation results reveal that the ego vehicle could safely and efficiently accomplish the driving task after learning and training.

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