Enhancing Sensor Fault Tolerance in Automotive Systems With Cost-Effective Cyber Redundancy

Abstract

In modern vehicles, there are hundreds of sensors, and many of them are safety-critical, which means a malfunction in their operation can cause catastrophic consequences. The conventional approach for the fault tolerance of these sensors is to use redundant sensors, which inevitably increases costs and overhead. To address this challenge, we propose a new perspective for redundant sensors, which we refer to as cyber-approximate sensors. The idea is that instead of relying solely on physical redundancy, we devise sensors favoring existing cyber facilities to create redundancy. Furthermore, recognizing that the redundant sensors do not need to be as accurate as the primary ones, we exploit an approximation-based model that incurs low overhead. To this end, our sensors employ inherent dependencies among vehicle sensors in two steps: i) identifying related dependencies and ii) designing a regression model. As a case study, we applied the cyber redundancy approach to a fuel control system and conducted fault injection experiments using the Hardware-in-the-Loop platform to analyze the fault tolerance. Since the performability metric, unlike reliability, can consider performance degradation, we employed the performability metric to evaluate fault tolerance. Indeed, reliability follows a binary nature, where a system is either correct or failed. However, vehicle sensors can exhibit varying degrees of functionality between perfect operation and complete failure. They might experience partial degradation, which can still be acceptable. Our experiments show that the proposed cyber redundancy approach not only reduces high-cost physical overhead (by roughly 50%) but also enhances performability (by approximately 7%).