Towards Efficient Oversight of Autonomous Vehicles: Causal Chain Fault Detection Using Minimal Grey Box

Highly Automated Vehicles (HAVs) present complex challenges for regulators and industry stakeholders due to their susceptibility to diverse faults and the risks posed by their black-box nature during real-world operations. Regulators play a critical role in ensuring the safety and reliability of HAV systems, which necessitates robust fault detection and oversight mechanisms. A critical regulatory task is to define a minimal yet sufficient set of data (i.e., a Minimal Grey Box) that regulators should collect to trace the causal chain of any collision or near-collision events. However, conventional approaches, such as Event Data Recorders (EDRs) and Data Storage Systems for Automated Driving (DSSAD), fall short in detecting HAV -specific faults, mainly due to the vast volume of data generated by these systems. Furthermore, existing fault detection methods are often inadequate in identifying software-related issues, largely because of the opaque, black-box characteristics of HAVs. To address the challenges posed by the sheer size of the datasets and the opacity of the systems under oversight, this study introduces a fault detection method that extracts a Minimal Grey Box (MGB). The proposed approach first learns the causal structure of HAV faults using Bayesian Network, which are subsequently mapped onto a fault tree. MGB variables are then identified from the Minimal Cut Sets (MCS) of the fault tree, providing a streamlined input for fault detection. In a case study using the Baidu Apollo as the system under oversight, 18 key variables were extracted from an initial set of 79 to construct the MGB, resulting in a 68.4% reduction in data storage. The fault detection results demonstrate the superior performance of the detection model based on the concise MGB, achieving high F1-scores (>95%) across 5 Apollo sub-modules. This approach highlights a promising solution to the efficient oversight and accurate fault detection in HAVs.