Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous Vehicles

Abstract

This paper presents a fail-safe algorithm for the exteroceptive sensors of autonomous vehicles. The proposed fault diagnosis mechanism consists of three parts: (1) fault detecting by a duplication-comparison method, (2) fault isolating by possible area prediction and (3) in-vehicle sensor fail-safes. The main ideas are the usage of redundant external sensor pairs, which estimate the same target, whose results are compared to detect the fault by a 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 they are from sensors themselves or a software error. In addition, faults were isolated by defining possible areas where existing sensor coordinates could be measured, 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 experiments were conducted and the performance of the suggested algorithm was evaluated based on the time interval between injection and the detection of faults.