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

arXiv: https://arxiv.org/abs/2407.21069 · HTML: https://mlsanity.com/paper/2407.21069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 28 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation](https://mlsanity.com/paper/2312.01728/) (2023)
- [Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach](https://mlsanity.com/paper/2210.11780/) (2022)
- [ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection](https://mlsanity.com/paper/1907.01051/) (2019)
- [Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns](https://mlsanity.com/paper/2205.09390/) (2022)
- [ML-driven Malware that Targets AV Safety](https://mlsanity.com/paper/2004.13004/) (2020)
- [Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner](https://mlsanity.com/paper/2405.03185/) (2024)
- [Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction](https://mlsanity.com/paper/1903.00667/) (2019)
- [Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix Factorization](https://mlsanity.com/paper/2203.10651/) (2022)
- [Kayotee: A Fault Injection-based System to Assess the Safety and Reliability of Autonomous Vehicles to Faults and Errors](https://mlsanity.com/paper/1907.01024/) (2019)

## Built on by (most cited first, 1 of 1)

- [LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2501.15850/) (2025)

## Abstract (arXiv)

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.