汽车电子研究中心
Research / Autonomous Driving Decision
Research Direction

The control system of electric vehicle(EV) system includes power driving system, energy management system, chassis electronic control system, lighting system, indicating instrument display system, auxiliary system, vehicle control system, air conditioning system and security system etc.. The control system is the core of the control in EV, which undertakes vehicle data exchange, vehicle safety management, Driver’s intention, power distribution management and energy distribution management. It is impact to economy, safety, driving comfort and dynamic coordinated control of vehicle.

The main research area is

1. Advanced vehicle dynamics control

2. Power distribution management strategy for electric vehicles

3. Energy distribution management strategy for electric vehicles

Reinforcement Learning in Autonomous Driving

Automatic driving technology is regarded as a powerful tool to solve traffic problems such as traffic accidents, congestion, and exhaust pollution. In this paper, we propose a new control strategy training method for self-driving vehicles, based on the deep reinforcement learning model. The method involves a Q-learning algorithm with filtered experience replay and pre-training with experiences from professional drivers, which accelerates the training process due to reduced exploration spaces. The method also involves resampling the state input after clustering, which improves the generalization ability of the strategy effectively, due to the individual and independent distribution of the samples. Experimental results demonstrate that the proposed model could reduce the time consumption of training by 92%, and the control stability increases by about 34%, compared with the existing neural fitted Q-iteration algorithm. In addition, procedure on resampling after clustering could increases the average travel distance by 73.4%, compared with the Q-learning algorithm with filtered experience replay that is based on the testing track which slightly more complicated than training set.

Intellectual Properties

Selected Papers

1.夏伟,李慧云*,基于深度强化学习的自动驾驶策略学习方法,集成技术,第6卷 第3期,2017 年5 月。
2.Wei Xia, Huiyun Li*, Baopu Li, “A Control Strategy of Autonomous Vehicles based on Deep Reinforcement Learning”,2016 9th International Symposium on Computational Intelligence and Design (ISCID 2016),Hangzhou,China,10-12 Dec,2016. 

Selected Patents