Copilot and CAN-Bus Connected ECUs Integrated Scenarios for Automotive Diagnosis, Enhanced by Scan Tools, Conversational Agents and AI Technologies
TL;DR
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, and consists of a set of procedures and steps that should be followed, for building an intelligent agent.
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.