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.

翻译：确保高度自动驾驶车辆（HAVs）的容错性对于其安全性至关重要，因为可能存在严重的故障。因此，从业者通过故障注入（FI）测试来评估HAVs的安全水平。为了全面覆盖测试用例，应考虑各种驾驶场景和故障设置。然而，由于测试场景和故障设置的组合众多，测试空间可能变得复杂且高维。此外，评估所有新增场景的性能消耗大量资源。能够引发安全问题的关键故障的稀有性进一步加剧了这一挑战。为应对这些挑战，我们提出在低秩平滑正则化矩阵分解（SRMF）框架下加速FI测试。我们首先将稀疏的评估数据根据其安全值组织成结构化矩阵。然后，通过矩阵结构捕获的相关性来估计未测试的值。为应对高维度问题，对测试空间施加了低秩约束。为了利用现有场景与新场景之间的关系并捕获关键故障的局部规律性，进一步设计了三种平滑正则化作为补充。我们在跟车和切入场景中进行了实验。结果表明，与其他机器学习模型相比，SRMF在各种场景中具有最低的预测误差，并且能够预测罕见的关键故障。此外，SRMF在识别关键故障时可以实现1171倍的加速率、99.3%的精确率和91.1%的F1分数。据我们所知，这是首次将低秩模型引入HAVs的FI测试的工作。