High-Dimensional Fault Tolerance Testing of Highly Automated Vehicles Based on Low-Rank Models

TL;DR

This paper introduces a low-rank matrix factorization approach to accelerate fault injection testing in highly automated vehicles, effectively predicting critical faults with high accuracy and significantly reducing testing resources.

Contribution

It is the first to apply low-rank models to fault injection testing of HAVs, improving efficiency and accuracy in identifying critical faults in high-dimensional testing spaces.

Findings

SRMF achieves the lowest prediction error among tested models.

It predicts rare critical faults with 99.3% precision.

The method accelerates testing by 1171 times.

Abstract

Ensuring fault tolerance of Highly Automated Vehicles (HAVs) is crucial for their safety due to the presence of potentially severe faults. Hence, Fault Injection (FI) testing is conducted by practitioners to evaluate the safety level of HAVs. To fully cover test cases, various driving scenarios and fault settings should be considered. However, due to numerous combinations of test scenarios and fault settings, the testing space can be complex and high-dimensional. In addition, evaluating performance in all newly added scenarios is resource-consuming. The rarity of critical faults that can cause security problems further strengthens the challenge. To address these challenges, we propose to accelerate FI testing under the low-rank Smoothness Regularized Matrix Factorization (SRMF) framework. We first organize the sparse evaluated data into a structured matrix based on its safety values. Then the untested values are estimated by the correlation captured by the matrix structure. To address high dimensionality, a low-rank constraint is imposed on the testing space. To exploit the relationships between existing scenarios and new scenarios and capture the local regularity of critical faults, three types of smoothness regularization are further designed as a complement. We conduct experiments on car following and cut in scenarios. The results indicate that SRMF has the lowest prediction error in various scenarios and is capable of predicting rare critical faults compared to other machine learning models. In addition, SRMF can achieve 1171 acceleration rate, 99.3% precision and 91.1% F1 score in identifying critical faults. To the best of our knowledge, this is the first work to introduce low-rank models to FI testing of HAVs.