  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.
