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 resourceconsuming. 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 lowrank 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, $\mathbf{9 9. 3 \%}$ precision and $\mathbf{9 1. 1 \%}$ 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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Mei, Yuewen (Autor:in) / Nie, Tong (Autor:in) / Sun, Jian (Autor:in) / Tian, Ye (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    3314357 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Challenges and Approaches for Testing of Highly Automated Vehicles

    Schöner, Hans-Peter | Springer Verlag | 2015


    Fault Tolerance Analysis of Car-Following Models for Autonomous Vehicles

    Awal, Tanveer / Mushfiq, Md. Masum / Islam, A. B. M. Alim Al | IEEE | 2022


    Challenges Of Testing Highly Automated Vehicles: A Literature Review

    Karunakaran, Dhanoop / Berrio, Julie Stephany / Worrall, Stewart et al. | IEEE | 2022