This study aims to enhance the efficiency of the after-sales service (A/S) process for commercial trucks by implementing a data-driven approach. Traditional A/S methods result in long repair wait times, especially for intermittent faults requiring symptom reproduction. To address this, a system that records Diagnostic Trouble Code (DTC) and Vehicle Running Mode (VRM) data at failure moments is proposed. By storing data from 10 seconds before and after an event, fault diagnosis can be performed without symptom reproduction. Additionally, for exported vehicles, stored data enables remote analysis, overcoming real-time data limitations due to varying environmental factors. This approach improves maintenance reliability, optimizes repair accuracy, and supports proactive quality improvements for newly developed vehicles.

This study aims to develop a Commercial Vehicle Integrated Traffic Safety System utilizing Connected Intelligent Transportation Systems (C-ITS) technology. This system provides functionalities for accident prevention and efficient traffic management through vehicle-to-vehicle and vehicle-to-infrastructure communications. The key findings suggest that the integrated system using C-ITS can offer functions for traffic safety and preliminary stages of autonomous driving. It is anticipated that by applying vehicle and Information and Communication Technology (ICT) technologies, traffic safety issues and driver convenience can be enhanced.