Diagnostic Fusion for Time-Triggered Automotive Networks

Modern vehicles with semi-autonomous (driver-assistance systems) and autonomous capabilities require sophisticated on-board and off-board diagnostics for safe operation, and to reduce unnecessary component replacements at the service garage. We present a diagnostic approach that strategically fuses different sources of instrumentation available in a time-triggered automotive network (Flex Ray) for vehicle control, and learns patterns or signatures of different faults. These patterns ease the classification of faults during runtime or in the service garage. We evaluate our approach through fault-injection experiments on an automotive test bench, and demonstrate that by fusing different sources of instrumentation we can diagnose protocol-level and physical faults with over 98% accuracy. We also show that our approach is applicable across different network topologies.