Copilot and CAN-Bus Connected ECUs Integrated Scenarios for Automotive Diagnosis, Enhanced by Scan Tools, Conversational Agents and AI Technologies

Cosmin Tomozei; Iulian Furdu; Bogdan Patrut

doi:10.15837/ijccc.2026.4.7501

Authors

Cosmin Tomozei Vasile Alecsandri University of Bacau, Romania
Iulian Furdu Vasile Alecsandri University of Bacau, Romania
Bogdan Patrut Alexandru Ioan Cuza University of Ias, i, Romania

DOI:

https://doi.org/10.15837/ijccc.2026.4.7501

Keywords:

ECUs, A.I. conversational agents, vehicle communication interfaces (VCIs), diagnostic trouble codes (DTCs), vehicle diagnosis

Abstract

The evolution of automotive technologies brought into play the large amount of hardware and software devices, with their subsequent software frameworks and programming languages, as well as the adoption of Cloud environments for data storage, management and analysis. In this general context, specialists are faced with information overload and users have become more and more confused about the measures that should be taken in order to optimize the vehicle use, maintenance and repair. AI technologies could greatly help both users and specialists in their vehicular interactions. On the one hand, AI could give very good answers as long as the information on which the responses are based is correct and complete. On the other hand, the questions should be well formulated, clear and specific in order to maximize the accuracy and correctness of the answers. This paper aims at building an algorithm of combining ISO 15765-4 and SAE J1979 compliant professional scan tools technologies with Microsoft Copilot for evolving the automotive diagnosis process. The resulting algorithm consists of a set of procedures and steps that should be followed, for building an intelligent agent. This agent incorporates a knowledge base that includes maintenance and repair manuals, diagnosis tests results and previous experience. In this way, the vehicle maintenance and repair activities should gain a higher level of efficiency.

References

Saqib H.; Thippa R. G.; Praveen K.; Reddy M.; Swarna P. R.; Parimala M.; Chamitha D. A.; Madhusanka L. Autonomous vehicles in 5G and beyond: A survey, Vehicular Communications, Volume 39, 2023, 100551, ISSN 2214-2096. https://doi.org/10.1016/j.vehcom.2022.100551

Johansson, K.; Törngren, M.; Nielsen, L. Vehicle Applications of Controller Area Network. 2005, 10.1007/0-8176-4404-0_32. https://doi.org/10.1007/0-8176-4404-0_32

Naeem H.; Mustafizur R.; Devarajan R. Artificial Intelligence-Driven Vehicle Fault Diagnosis to Revo-lutionize Automotive Maintenance: A Review, Computer Modelling in Engineering & Sciences 2024, 141(2), pp.951-996. https://doi.org/10.32604/cmes.2024.056022

What Is the Future of the Automotive Industry with Agentic AI? https://www.gnani.ai/resources/blogs/top-automotive-ai-trends-redefining-customerexperience/ (accessed on 04 July 2025)

Azevedo Takara, L.; Cocco Mariani,V.; Santos Coelho, L. Novel engine fault diagnosis framework based on machine learning and MiniRocket feature extraction using multi-correlation feature selection and predictive power score, Expert Systems with Applications, Volume 293, 2025,128662, ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2025.128662

Mode, G.R.; Adversarial robustness of deep learning enabled industry 4.0 prognostics, 2020.

Tomozei, C.; Furdu, I.; Mâţă, L. Automotive sensors in classroom, guiding students for understanding complex architectures, 2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC), Novi Sad, Serbia, pp. 73-76, 25-26 May 2022. https://doi.org/10.1109/ZINC55034.2022.9840587

Furdu, I.; Tomozei, C.; Kose, U. Pros and Cons Gamification and Gaming in Classroom. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 8 (2), pp.56-62, 2017, https://brain2.edusoft.ro/index.php/brain/article/view/238 (accessed on 04 July 2025).

Rasheed, R.; Qazi, F.; Dur e Shawar Agha; Ahmed, A.; Asif, A.; Shams, H. Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review. VFAST Transactions on Software Engineering, 12(1), pp.169-182, 2024. https://doi.org/10.21015/vtse.v12i1.1713

Bosch Automotive Electrics and Automotive Electronics: Systems and Components, Networking and Hybrid Drive, 2014, Springer Fachmedien Wiesbaden, pp. 1-9, ISBN 978-3-658-01784-2.

Butilă, E.V.; Boboc, R.G. Mapping the Landscape of Romanian Automotive Research: A Bibliometric Analysis. Vehicles 2025, 7, 31. https://doi.org/10.3390/vehicles7020031

https://www.iso.org/standard/67245.html (accessed on 04 July 2025)

ZEVonUDS - Zero Emission Vehicle SAE J1979-3 Diagnostic Standard|Vector

Ryu, YH., Lee, KW., Sung, DU. et al. Review of diagnosis technology for future mobility vehicle. JMST Adv. 5, 77-84 (2023). https://doi.org/10.1007/s42791-023-00056-8

Meléndez-Useros M., Viadero-Monasterio F., Jiménez-Salas M., López-Boada M. J., Active steering fault diagnosis via integrated LSTM-based sensor detection and robust actuator fault estimation, Relia-bility Engineering & System Safety, Volume 265, Part A, 2026,111573, ISSN 0951-8320. https://doi.org/10.1016/j.ress.2025.111573

Li, Y.; Liu, W.; Liu, Q.; Zheng, X.; Sun, K.; Huang, C. Complying with ISO 26262 and ISO/SAE 21434: A Safety and Security Co-Analysis Method for Intelligent Connected Vehicle. Sensors 2024, 24, 1848. https://doi.org/10.3390/s24061848

Lin, Z., Ziyang, Z. (2023). Summary of Fault Diagnosis of Electric Vehicle Braking System. Proceedings of TEPEN 2022. TEPEN 2022. Mechanisms and Machine Science, vol 129. Springer, Cham. https://doi.org/10.1007/978-3-031-26193-0_18

KVASER, Advanced connectivity and CAN solution for engineering, https://kvaser.com/aboutcan/ can-standards/j2534/ (accessed on 20 July 2025)

Bosch Mobility Solutions https://www.bosch-mobility-solutions.com/en/company/pressreleases/ software-and-electronics-expertise/, 2021 (accessed on 20 July 2025)

AUTEL, Research and Development for Automotive Diagnosis, https://www.auteltech.com/u/cms/www/202405/21040935iehs.pdf (accessed on 04 July 2025)

Allouch, M.; Azaria, A.; Azoulay, R. Conversational Agents: Goals, Technologies, Vision and Challenges. Sensors, 2021, 21, 8448. https://doi.org/10.3390/s21248448

Raya, M., Hubaux, J.-P. (2007). Securing vehicular ad hoc networks. Journal of Computer Security, 15(1), 39-68. https://doi.org/10.3233/JCS-2007-15103

MATLAB Embedded Coder https://www.mathworks.com/products/embedded-coder.html (accessed on 23 July 2025)

Automotive Open Systems Architecture, https://www.autosar.org/(accessed on 23 July 2025)

Re, L., Yuhao, A., Zhenghua, L., Dengta, N., Jinlong, D. (2024). Fault Diagnosis Method of Launch Vehicle Based on Deep Neural Network Proceedings of 2024 Chinese Intelligent Systems Conference. CISC 2024. Lecture Notes in Electrical Engineering, vol 1285. Springer, Singapore. https://doi.org/10.1007/978-981-97-8658-9_54

Ford Technical Information System, https://www.etis.ford.com(accessed on 23 July 2025)

Forscan Forum, https://www.forscan.org/forum/(accessed on 23 July 2025)

Haynes Digital Manual, https://haynes.com/en-gb/ford/mondeo/2008-2010-20-diesel-272455, Haynes Publishing Group, 2019, ISBN 178521442X.

Audatex Autodata, https://www.solera.com/ (accessed on 24 July 2025)

Md Naeem Hossain; Md Mustafizur Rahman; Devarajan Ramasamy. Artificial Intelligence Driven Vehicle Fault Diagnosis to Revolutionize Automotive Maintenance: A Review. Computer Modelling in Engineering & Sciences, Vol. 141, No. 2, pp. 951-996, 2024. https://doi.org/10.32604/cmes.2024.056022

Andrioaia, D, Găitan V, Pătruţ B., Furdu I (2024) - Comparative Performance Analysis of Filling Missing Values Algorithms in PdM Systems of UAV, BRAIN. Broad Research in Artificial Intelligence and Neuroscience, Vol. 15, No. 2, pp. 437-453. https://doi.org/10.18662/brain/15.2/561

Shafique, S., Abid, S., Riaz, F., Ahmed, B., Khan, M., Younis, U., Hamza, A. (2025). CARLODD: A Vision Benchmark Dataset of Asia for On-Road Vehicle Detection and Recognition. BRAIN. Broad Research In Artificial Intelligence And Neuroscience, 16(4), pp. 169-184. https://doi.org/10.70594/brain/16.4/10