TY  - JOUR
PY  - 2020//
TI  - Deep reinforcement learning based left-turn connected and automated vehicle control at signalized intersection in vehicle-to-infrastructure environment
JO  - Information (Basel)
A1  - Chen, Juan
A1  - Xue, Zhengxuan
A1  - Fan, Daiqian
SP  - e77
EP  - e77
VL  - 11
IS  - 2
N2  - In order to solve the problem of vehicle delay caused by stops at signalized intersections, a micro-control method of a left-turning connected and automated vehicle (CAV) based on an improved deep deterministic policy gradient (DDPG) is designed in this paper. In this paper, the micro-control of the whole process of a left-turn vehicle approaching, entering, and leaving a signalized intersection is considered. In addition, in order to solve the problems of low sampling efficiency and overestimation of the critic network of the DDPG algorithm, a positive and negative reward experience replay buffer sampling mechanism and multi-critic network structure are adopted in the DDPG algorithm in this paper. Finally, the effectiveness of the signal control method, six DDPG-based methods (DDPG, PNRERB-1C-DDPG, PNRERB-3C-DDPG, PNRERB-5C-DDPG, PNRERB-5CNG-DDPG, and PNRERB-7C-DDPG), and four DQN-based methods (DQN, Dueling DQN, Double DQN, and Prioritized Replay DQN) are verified under 0.2, 0.5, and 0.7 saturation degrees of left-turning vehicles at a signalized intersection within a VISSIM simulation environment. The results show that the proposed deep reinforcement learning method can get a number of stops benefits ranging from 5% to 94%, stop time benefits ranging from 1% to 99%, and delay benefits ranging from −17% to 93%, respectively compared with the traditional signal control method.<p />  <p>Language: en</p>
LA  - en
SN  - 2078-2489
UR  - http://dx.doi.org/10.3390/info11020077
ID  - ref1
ER  -