Software-Based Fault-Detection Technique for Object Tracking in Autonomous Vehicles

Autonomous vehicles are nowadays gaining popularity in many different sectors, from automotive to aviation, and find application in increasingly complex and strategic contexts. In this domain, Obstacle Detection and Avoidance Systems (ODAS) are crucial and, since they are safety-critical systems, they must employ fault-detection and management techniques to maintain correct behavior. One of the most popular techniques to obtain a reliable system is the use of redundancy, both at the hardware and at the software levels. With the objective of improving fault-detection while producing little impact on the programmability of the system, this paper introduces a general and lightweight monitoring technique based on a user-directed observer design pattern, which aims at monitoring the validity of predicates over state variables of the algorithms in execution. This can increase the fault-detection capability and even anticipate the detection time of some faults that would be caught by replication only at later times. Results are evaluated on a real-world use-case from the railway domain, and show how the proposed fault-detection mechanism can increase the overall reliability of the system by up to 24.4% compared to replication alone in case of crowded scenarios over the entire tracking process, and up to 43.9% in specific phases.