A Fault Detection and Diagnosis System for Autonomous Vehicles Based on Hybrid Approaches

An accurate fault detection and diagnosis system is of great importance for autonomous vehicles to prevent the potential hazardous situations. In this paper, we propose a fault detection and diagnosis system based on hybrid approaches. First, to detect the state faults of the autonomous vehicle, One-Class Support Vector Machine (SVM) method is adopted to train the boundary curve which separates the safe domain and unsafe domain. Meanwhile, a Kalman filter observer is designed based on the linear kinematic vehicle bicycle model to predict the current position of the vehicle, and after obtaining the residuals between prediction and measurement, Jarque-Bera test is applied to check the normality of the residuals probability distribution to monitor whether the trajectory deviates. Furthermore, we design a fuzzy system to distinguish the types of the detected faults based on a modified neutral network, in which a membership function layer is added after the input layer. With the strong self-learning ability of neutral network, the initial membership function of the fuzzy system is updated through black box test and can indicate the probability of each fault type. Experiments on the real autonomous vehicle platform 'Xinda' and performance comparison with other fault detectors validate the effectiveness of these methods and the usability of the fault detection and diagnosis system.