A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis

In the automotive industry, the full cycle of managing in-use vehicle quality issues can take weeks to investigate. The process involves isolating root causes, defining and implementing appropriate treatments, and refining treatments if needed. The main pain-point is the lack of a systematic method...

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Bibliographic Details
Main Authors Wang, Qian, Shui, Huanyi, Tran, Thi Tu Trinh, Nezhad, Milad Zafar, Upadhyay, Devesh, Paynabar, Kamran, He, Anqi
Format Journal Article
LanguageEnglish
Published 31.03.2023
Subjects
Computer Science - Artificial Intelligence
Computer Science - Learning
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Summary:In the automotive industry, the full cycle of managing in-use vehicle quality issues can take weeks to investigate. The process involves isolating root causes, defining and implementing appropriate treatments, and refining treatments if needed. The main pain-point is the lack of a systematic method to identify causal relationships, evaluate treatment effectiveness, and direct the next actionable treatment if the current treatment was deemed ineffective. This paper will show how we leverage causal Machine Learning (ML) to speed up such processes. A real-word data set collected from on-road vehicles will be used to demonstrate the proposed framework. Open challenges for vehicle quality applications will also be discussed.
DOI:10.48550/arxiv.2304.04755