Fault Tolerance by Data Fusion: A Case Study.

Summary Multi-sensor perception systems are beginning to appear in mission-critical applications, such as ADAS systems in automobiles. These systems, which are complex and based on artificial intelligence methods, are difficult and expensive to validate. In this paper, we study an alternative method of dependability: fault tolerance. We study fault tolerance as directly enabled by data fusion, and then propose detection and recovery mechanisms suitable for multi-sensor perception. By analyzing some parameters related to data fusion, we propose different services for the dependability of perception systems, such as detection, recovery by compensation, and fault hiding.