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A sensorless state estimation for a safety-oriented cyber-physical system in urban driving: Deep learning approach
IEEE/CAA Journal of Automatica Sinica ( IF 11.8 ) Pub Date : 2020-11-26 , DOI: 10.1109/jas.2020.1003474
Mohammad Al-Sharman , David Murdoch , Dongpu Cao , Chen Lv , Yahya Zweiri , Derek Rayside , William Melek

In todayʼ s modern electric vehicles, enhancing the safety-critical cyber-physical system ( CPS ) ʼ 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 vehicleʼ 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.

中文翻译:

城市驾驶中面向安全的网络物理系统的无传感器状态估计:深度学习方法

在当今的现代电动汽车中,提高安全性至关重要的物理网络系统(CPS)的性能对于车辆的安全机动性是必不可少的。作为典型的CPS,制动系统对于车辆设计和安全控制至关重要。然而,期望制动压力的精确状态估计以高度自主地执行安全驾驶。本文采用深度学习的方法,开发了一种无传感器的车辆制动压力状态估计技术。深度神经网络(DNN)使用深度学习训练技术(如辍学和矫正单元)进行构造和训练。这些技术用于获得更准确的模型,用于制动压力状态估计应用。使用实际实验训练数据对建议的模型进行训练,这些数据是通过进行实际车辆测试收集的。车辆安装在底盘测功机上,同时在随机行驶周期下收集制动压力数据。基于这些实验数据,对DNN进行了训练,并相应地验证了所提出的状态估计方法的性能。结果表明,RMSE为0.048 MPa的高精度制动压力状态估计。
更新日期:2020-11-27
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