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.
@article{arxiv.2007.08343,
title = {Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study},
author = {Teng Liu and Xingyu Mu and Xiaolin Tang and Bing Huang and Hong Wang and Dongpu Cao},
journal= {arXiv preprint arXiv:2007.08343},
year = {2020}
}