Collecting and Analyzing Automotive Crash Data Using in-Vehicle Network Traffic and Infotainment System Logs

Modern vehicles are increasingly equipped with complex digital electronic systems that facilitate connectivity, improve safety, and support intelligent driving features. This evolution has introduced new opportunities for post-crash analysis using the vast data generated by internal vehicle systems. Traditional methods relying solely on Event Data Recorders (EDRs) often face limitations due to proprietary formats and restricted accessibility. In this study, we propose a novel forensic investigation framework that leverages Controller Area Network (CAN) traffic and In-Vehicle Infotainment (IVI) system logs to collect and analyze automotive crash data. By combining these two data sources, the proposed approach enables a more comprehensive reconstruction of crash events without the need for manufacturer-specific tools. Key vehicle and occupant-related parameters, such as speed, throttle position, RPM, seatbelt usage, airbag deployment, and door status, can be extracted and synchronized for in-depth analysis. To validate our method, we conducted experiments using a Hyundai Avante CN7 and successfully demonstrated that integrated CAN and IVI log analysis offers valuable insights into crash scenarios. The results indicate that this approach not only enhances crash diagnostics but also provides reliable digital evidence for legal proceedings and liability assessments. This study contributes to the advancement of vehicle forensics by promoting accessible, multi-source data analysis techniques for intelligent transportation systems.