Design and implementation of an adaptive cruise control system based on supervised actor-critic learning

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 深度强化学习

	Design and implementation of an adaptive cruise control system based on supervised actor-critic learning
	Wang,Bin; Zhao,Dongbin; Li,Chengdong; Dai,Yujie
	2015
会议名称	The 5th International Conference on Information Science and Technology (ICIST 2015)
会议日期	April 24–26, 2015
会议地点	Hunan, China
摘要	A novel adaptive cruise control (ACC) system is proposed in this paper. A hierarchical control framework is adopted for the adaptive cruise control problem. For the upper level, a supervised actor-critic (SAC) reinforcement learning approach is presented to obtain the desired acceleration controller. In the lower level, throttle and brake controllers calculate the required throttle and/or brake signals based on the desired longitudinal acceleration. Feed-forward neural networks are used to implement the actor and critic components of the SAC learning algorithm. An online learning mechanism is introduced to implement the SAC training process. dSPACE simulator is used to verify the effectiveness of the ACC system. Typical emergency braking scenario is simulated to test the adaptability of the ACC system. Road condition change (e.g. wintry or wet conditions) simulation is first investigated to evaluate the robustness of the ACC system. Performance of the proposed ACC system is proved to be very practical.
关键词	Adaptive Cruise Control System Level Control Acceleration Control
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/19887
专题	多模态人工智能系统全国重点实验室_深度强化学习
推荐引用方式 GB/T 7714	Wang,Bin,Zhao,Dongbin,Li,Chengdong,et al. Design and implementation of an adaptive cruise control system based on supervised actor-critic learning[C],2015.