Simultaneous Estimation of Sensor Faults and Road Parameters in Production Vehicles

Detecting sensor faults in automotive systems is vital to ensure the safe operation and reliability of vehicle controllers. The aim is to detect faults reliably and quickly while avoiding false positives. In particular, the road parameters in the form of banking angle and friction coefficient strongly influence the fault estimation. This work presents a framework for the reliable detection of sensor faults in the vehicle's Inertial Measurement Unit (IMU) up to the driving dynamics limit range. The unknown road parameters are estimated in parallel and enable the use of the proposed method under varying environmental conditions. The estimation of the vehicle states, the coefficient of friction and the sensor faults is carried out using a Two-Stage Unscented Kalman Filter with a nonlinear vehicle model. The estimation of the road inclination and banking angle is carried out using a separate Extended Kalman Filter. Measurements with an Audi SQ8 e-tron under high and low friction condition are presented and show a fault detection time of 100 ms while avoiding false-positive detections for quasi steady state friction conditions. Rapid variations in the coefficient of friction lead to a slight degradation in fault estimation performance.