A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving: Deep Learning Approach

doi:10.1109/JAS.2020.1003474

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	A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving: Deep Learning Approach
	Mohammad Al-Sharman; David Murdoch; Dongpu Cao; Chen Lv; Yahya Zweiri; Derek Rayside; William Melek
刊名	IEEE/CAA Journal of Automatica Sinica
	2021
卷号	8 期号:1 页码:169-178
关键词	Brake pressure state estimation cyber-physical system (CPS) deep learning dropout regularization approach
ISSN号	2329-9266
DOI	10.1109/JAS.2020.1003474
英文摘要	In today’s modern electric vehicles, enhancing the safety-critical cyber-physical system (CPS)’s performance is necessary for the safe maneuverability of the vehicle. As a typical CPS, the braking system is crucial for the vehicle design and safe control. However, precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy. In this paper, a sensorless state estimation technique of the vehicle’s brake pressure is developed using a deep-learning approach. A deep neural network (DNN) is structured and trained using deep-learning training techniques, such as, dropout and rectified units. These techniques are utilized to obtain more accurate model for brake pressure state estimation applications. The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing. The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles. Based on these experimental data, the DNN is trained and the performance of the proposed state estimation approach is validated accordingly. The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/43905]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Mohammad Al-Sharman,David Murdoch,Dongpu Cao,et al. A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving: Deep Learning Approach[J]. IEEE/CAA Journal of Automatica Sinica,2021,8(1):169-178.
APA	Mohammad Al-Sharman.,David Murdoch.,Dongpu Cao.,Chen Lv.,Yahya Zweiri.,...&William Melek.(2021).A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving: Deep Learning Approach.IEEE/CAA Journal of Automatica Sinica,8(1),169-178.
MLA	Mohammad Al-Sharman,et al."A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving: Deep Learning Approach".IEEE/CAA Journal of Automatica Sinica 8.1(2021):169-178.