This paper describes a longitudinal model based probabilistic fault diagnosis algorithm of autonomous vehicles using sliding mode observer. Autonomous vehicles use various sensors such as radar, lidar, and camera to obtain environment information. And internal sensors such as wheel speed, acceleration, and steering angle sensors have been used in vehicle to measure vehicle dynamic states. Based on the measured environment and vehicle states information, autonomous vehicle decides how to drive and control steering, throttle, and brake. Therefore, fault diagnosis of sensors used in autonomous vehicles is the most important for safe driving. In order to diagnosis longitudinal acceleration sensor fault of autonomous vehicle, longitudinal kinematic model has been used. The relative acceleration has been reconstructed using sliding mode observer based on environment information such as relative displacement and velocity between preceding vehicle and subject vehicle. The reconstructed relative acceleration has been used to compute longitudinal acceleration probabilistically based on analyzed longitudinal vehicle’s acceleration. The computed acceleration has been compared with measured acceleration for fault diagnosis of the acceleration sensor. The probabilistic fault diagnosis algorithm has been proposed and evaluated using actual data with arbitrary fault signal. The evaluation results of the proposed fault diagnosis algorithm show the reasonable fault diagnosis performance.
A Sliding Mode Observer Approach for Attack Detection and Estimation in Autonomous Vehicle Platoons using Event Triggered Communication
