Multi-Sensors Fault Detection and Isolation for Cooperative Control in Highway Merging

This paper proposes a fault tolerant highway insertion management approach for Automative Driving (AD) system. The principle is as follows: the AD system in the ego vehicle (vehicle in the main lane) proposes an insertion decision to the merging vehicle based on the comparison between: i) the interdistance between the ego-vehicle itself and the merging vehicle and ii) a decision threshold. This decision threshold is computed by the ego-vehicle from the speed of the ego-vehicle, the speed of the merging vehicle communicated by the merging vehicle to the ego-vehicle and other parameters (such as safety margin, vehicles lengths,…). Since the insertion decision threshold depends on the speed measurements, adding a diagnostic layer to the merging vehicle speed estimation is crucial to obtain a safe and accurate insertion decision. With the merging vehicle's sensors measurements (GPS, wheel encoder, and speed sensor) sent to the ego-vehicle, the ego-vehicle can compute three estimates of the merging vehicle's speed: 1/ one is directly measured by a speed sensor, 2/ the other two are computed using Extended Kalman Filters (EKF) and data from respectively the GPS and the wheels encoders. Depending on the estimate of the current dynamic driving maneuver, the prediction model used in the EKFs switchs from a simple constant acceleration (CA) evolution model to a maneuvering model integrating the jerk estimation. Finally, based on the three redundant velocity estimates, residuals sensitive to sensor fault are generated. From the signature matrix, faulty measurements (if any) can be isolated and excluded of the estimation process.