Fail-Safe Algorithm for Sensors of Autonomous Vehicle

Abstract

This paper presents a fail-safe algorithm for extetoceptive sensors of autonomous vehicle. The proposed fault diagnosis mechanism consists of three parts: 1) fault detecting by duplication-comparison method, 2) fault isolating by possible area prediction and 3) in-vehicle sensor fail-safe. The main ideas are the usage of redundant external sensor pairs, which estimates the same target, whose results were compared to detect the fault. The faults are detected by modified duplication-comparison method and the novel fault isolation method using target predictions. By comparing the estimations of surrounding vehicles and the raw measurement data, the location of faults can be determined whether it is from sensors themselves or a software error. In addition, faults were isolated by defining Possible Area that measurements could exist on sensor coordinate, which can be predicted by using previous estimation results. The performance of the algorithm has been tested by using offline vehicle data analysis via Matlab. Various fault injection experiment were conducted and the performance of the suggested algorithm was evaluated based on the time interval between injection and the detection of faults.