A Predictive Approach to the Fault Detection in Fail-Safe System of Autonomous Vehicle Based on the Multi-Sliding Mode Observer

This paper describes a predictive method for fault detection in the fail-safe system of autonomous vehicles based on the multi sliding mode observer. In order to detect faults in sensors, such as radar and acceleration sensors used for longitudinal control of the autonomous vehicles, the kinematic model-based sliding mode observer and a predictive algorithm have been used. The driving condition that the subject vehicle is driving with a preceding vehicle has been considered in this study. The relative acceleration has been reconstructed based on the sliding mode observer using relative displacement and velocity. Based on the reconstructed relative acceleration, the upper and lower limits of longitudinal acceleration for fault detection have been derived based on the stochastic analysis of the driver’s driving data. The measured longitudinal acceleration of the subject vehicle has been used to predict the relative states using the longitudinal kinematic model. The predicted relative states have been stored, and the stored states that represent the current states have been used to detect faults in the sensors. With regard to longitudinal acceleration, the multi sliding mode observer has been used to detect faults in the acceleration sensor. The predictive fault detection algorithm proposed in this study can detect faults in the environment sensors individually based on past sensor information. In order to obtain a reasonable performance evaluation, actual driving data and a 3D full vehicle model constructed in the Matlab/Simulink environment have been used in this study. The results of the performance evaluation show that the predictive fault detection algorithm was successfully able to detect faults in the sensors for longitudinal control individually.