عنوان انگلیسی مقاله:
Automated vehicle’s behavior decision making using deep reinforcement learning and high-fidelity simulation environment
ترجمه فارسی عنوان مقاله:
تصمیم گیری خودکار وسیله نقلیه با استفاده از یادگیری تقویتی عمیق و محیط شبیه سازی با وفاداری بالا
Sciencedirect - Elsevier - Transportation Research Part C, 107 (2019) 155-170: doi:10:1016/j:trc:2019:08:011
Yingjun Ye, Xiaohui Zhang, Jian Sun⁎
Automated vehicles (AVs) are deemed to be the key element for the intelligent transportation
system in the future. Many studies have been made to improve AVs’ ability of environment
recognition and vehicle control, while the attention paid to decision making is not enough and
the existing decision algorithms are very preliminary. Therefore, a framework of the decisionmaking
training and learning is put forward in this paper. It consists of two parts: the deep
reinforcement learning (DRL) training program and the high-fidelity virtual simulation environment.
Then the basic microscopic behavior, car-following (CF), is trained within this framework.
In addition, theoretical analysis and experiments were conducted to evaluate the proposed
reward functions for accelerating training using DRL. The results show that on the premise
of driving comfort, the efficiency of the trained AV increases 7.9% and 3.8% respectively compared
to the classical adaptive cruise control models, intelligent driver model and constant-time
headway policy. Moreover, on a more complex three-lane section, we trained an integrated
model combining both CF and lane-changing behavior, with the average speed further growing
2.4%. It indicates that our framework is effective for AV’s decision-making learning.
Keywords: Automated vehicle | Decision making | Deep reinforcement learning | Reward function