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@article{197641,
        author = {Dipal Pimple and Siddhant Pal and Priyanshu Mishra and Dr. Sheetal Rathi and Mrs. Sonali Gandhi},
        title = {AI-Powered Vehicle Diagnostics: Trends, Gaps, and Opportunities},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {8354-8359},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197641},
        abstract = {Traditional vehicle diagnostic methods rely on onboard diagnostics (OBD) that produce cryptic error codes, requiring expert interpretation. Furthermore, non- expert vehicle owners struggle to accurately describe symptoms, leading to diagnostic ambiguity and delays. This paper presents an AI-driven vehicle diagnostic system that leverages a transformer-based Natural Language Processing (NLP) model to interpret user-submitted, free- text descriptions of vehicle issues. The system processes natural language inputs to identify the probable fault part and recommend a corresponding repair solution. By training a BERT/DistilBERT model on a historical fault dataset, our framework automates the initial diagnostic process. The architecture features a web/mobile interface for user interaction, an AI core for inference, and a backend for data storage and trend visualization. Performance evaluation on a test dataset indicates a high F1-score for fault classification, demonstrating the viability of using advanced NLP to make vehicle diagnostics more accessible and efficient.},
        keywords = {Vehicle Diagnostics, Machine Learning, Natural Language Processing (NLP), Fault Prediction, BERT, DistilBERT, Predictive Maintenance, Automotive AI.},
        month = {April},
        }