Bayesian Fault Injection Safety Testing for Highly Automated Vehicles With Uncertainty

Highly Automated Vehicles (HAVs) are exposed to numerous unexpected faults that threaten the functionality of the Autonomous Driving System (ADS) in HAVs, and even minor faults can lead to serious consequences such as collisions. Accordingly, fault tolerance of HAVs should be thoroughly evaluated before large-scale deployment. Fault Injection (FI) testing is commonly used for the verification and validation of HAVs. However, due to the time cost of FI simulation, it is impossible to simulate all combinations of initial conditions and the high-dimensional fault space. Meanwhile, the inherent uncertainty in complex ADS of the HAV under test cause uncertain testing results, which leads to unreliable results in one-time simulation. To address these problems, an accelerated FI method considering uncertainty within ADS based on Dynamic Bayesian Network (DBN) is proposed. DBN is applied to serve as a surrogate for HAVs and to learn the causal relationship between factors in the complex system of HAVs. Rolling Forecast and Monte Carlo sampling are combined to predict the collision probability after FI. Taking open-source Autonomous Driving System Baidu Apollo as the System Under Test, the experimental results demonstrate that the DBN-based FI method performs well both in efficiency and accuracy across various scenarios. DBN-based FI is 447 times faster than simulation and can achieve 88.9% precision. Furthermore, the collision probability and the variable distribution calculated by uncertain prediction are close to those obtained by simulation.