A sensorless state estimation for a safety-oriented cyber-physical system in urban driving: Deep learning approach

Abstract

In today's modern electric Vehicles, enhancing the Safety-Critical Cyber-Physical (CPS) system's performance is necessary for the safe maneuverability of the vehicle. As a typical CPS system, 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 special 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 highaccuracy brake pressure state estimation with RMSE of 0.048 MPa.

Self-Learned Autonomous Driving at Unsignalized Intersections: A Hierarchical Reinforced Learning Approach for Feasible Decision-Making

“…In the context of urban driving at intersections, the behavioural path planner is responsible for selecting a driving behaviour for unsignalized intersection-traversal maneuvers based on the environment conditions, including the unknown states and hidden variables of the intersection users which may generate multiple possible trajectories. Finally, this driving behaviour has to be mapped to a trajectory which will be tracked by a feedback controller considering aspects of the vehicle's dynamic model constraints, the guarantee of ride comfort, and safety [7], [8].…”

Pressure Estimation of the Electro-Hydraulic Brake System Based on Signal Fusion

“…However, the training method for conventional back propagation suffers from the problems of overfitting, a vanishing gradient as well as higher computational complexity in training. To this end, in [28], a deep neural network (DNN) was structured and was trained using deep-learning training techniques, such as dropout and rectified units, and a more accurate estimation was finally obtained. In [29], a time-series model based on multivariate deep recurrent neural networks (RNN) with long short-term memory (LSTM) units was developed for brake pressure estimation.…”

Automatic Recognition of Communication Signal Modulation Based on the Multiple‐Parallel Complex Convolutional Neural Network

“…Considering the characteristics of the modulation pattern of the communication signal and the size of the timefrequency map, the CNN contains a convolutional layer and a pooling layer to extract the effective feature vector; the nonlinearity of the network model is provided by the activation function Re LU used after each convolutional layer, and using the Re LU function as the activation function can suppress the gradient disappearance or explosion that occurs during the training of the network; the last layer is the final layer and is a fully connected layer used to integrate local features to obtain global features of the input data; finally, the global features are classified and identified by using the SOFTMAX activation function [29][30][31][32][33]. The following will illustrate the effects of different neural network structures on the classification recognition of modulated signals from three aspects: the number of nonconvolutional layers, the input size, and the convolutional kernel size.…”