@article{ref1,
title="A preliminary study of the recorded data in the conventional vehicles to identify the responsibility for the autonomous vehicle accidents",
journal="Transactions of the Korean Society of Automotive Engineers",
year="2023",
author="Park, Jongjin and Jeon, Oc-Yeub and Park, Jungwoo and Lee, Jung-Hwan",
volume="31",
number="8",
pages="637-642",
abstract="When a traffic accident involving a conventional vehicle occurs, information would be usually collected from Freeze Frame Data(FFD), Digital Tacho Graph(DTG), Event Data Recorder(EDR), Video Data Recorder(VDR), CCTV, and Telematics. Moreover, these sources of information, help detect the cause of the accident. In preparation for the emerging era of self-driving cars, the National Forensic Service(NFS) has exerted great efforts to determine who is responsible for such accidents based on these various data and DSSAD(Data Storage System for Automated Driving) information. NFS classified and analyzed 366 accidents(2015~2020) involving conventional vehicles that were equipped with EDR. Events were then recorded according to the codebook of the Initiative for the Global harmonization of Accident Data(IGLAD) to extract representative accident types. We are conducting research to respond to traffic accidents that can be preemptively predicted in the era of autonomous vehicles, such as developing various accident scenarios. In this study, we are introducing analysis cases of traffic accidents involving a vehicle with ADAS and Level 2 or a vehicle with higher safety specifications. It is intended to help in the development of autonomous vehicle safety systems, regulations, and driver's traffic safety measures.  	 Keywords: Digital tacho graph, Event data recorder, Data storage system for automated driving, Advanced driver assistance system, Autonomous vehicle, Traffic accident investigation 키워드: 전자식운행기록계, 사고기록장치, 자율주행 사고기록장치, 첨단운전자지원시스템, 자율주행자동차, 교통사고조사<p /> <p>Language: en</p>",
language="en",
issn="1225-6382",
doi="10.7467/KSAE.2023.31.8.637",
url="http://dx.doi.org/10.7467/KSAE.2023.31.8.637"
}