• 자동차안전학회지
• /
• 제15권1호
• /
• pp.35-44
• /
• 2023

The purpose of this study is to predict how the actual accident changes by reconstructing the accident with an Autonomous Emergency Braking system (AEB) based on the actual accident of the LTAP-OD (Left Turn Crossing Path - Opponent Direction) intersection. A virtual AEB sensor was developed, and 150 head-on collision accident reports were secured to the insurance company to reconstruct the accident. As a result of the accident type analysis, a total of 13 types of head-on collision accidents were derived, and it is the LTAP-OD intersection accident with the highest frequency. In the LTAP-OD intersection accident, the simulation was conducted by applying the virtual AEB of each vehicle, the accident rate decreased by 90% or more when the AEB of the left-turn vehicle was applied, and the accident rate decreased by 50%. In addition, the most frequent collision types in LTAP-OD accidents were the front bumper on the driver's side of a vehicle going straight and the front bumper on the passenger's side of a vehicle turning left.

• 한국인터넷방송통신학회논문지
• /
• 제15권4호
• /
• pp.239-247
• /
• 2015

최근에 자동차 사고발생 빈도수가 높은 정면충돌사고와 추돌사고에 대해 사회적 관심도 높아지면서 다양한 연구들이 진행되고 있다. 본 연구에서는 자동차 충돌사고에서 재구성을 위한 유효충돌속도와 인체상해발생정도의 관계를 과학적으로 분석하여 관련 모형식을 제시하였으며, 인체상해가 가능한 자동차 실차실험의 한계가 명확하므로 이를 대신하여 각종 충돌실험자료와 인체실험자료 등을 심층 분석하고자 하였다. 그 결과로 정면 및 추돌사고의 경우에는 유효 충돌속도가 7 km/h 이하에서는 상해가 발생하지 않는 임계치를 나타냈으며, 충돌속도와 소성변형량 정도를 통해 상해정도가 유효충돌속도에 선형적으로 비례하는 모형식을 제시하였다. 따라서 본 연구를 통하여 새롭게 제시된 유효충돌속도와 상해발생정도의 추정모형은 사고재구성에서 최소한의 공학적 판단기준으로 활용 가능하여 법적 분쟁시 유익한 정보를 제공할 것으로 사료된다.

• 자동차안전학회지
• /
• 제12권3호
• /
• pp.52-60
• /
• 2020

Usage of EDR(Event Data Recorder) report for traffic accident analysis is currently increasing due to government regulation of EDR data release. Nevertheless, a lot of investigators simply adopt by comparing the number of ignition cycles(crash) at event to the number of ignition cycles(download) without an exact judgment whether event data occurred by this accident or not. In the EDR report, besides ignition cycles, there are many factors such as event record type, algorithm active(rear/rollover/side/frontal), time between events, event severity status(rollover/rear/right side/reft side/frontal), belt switch circuit status, driver/passenger pretensioner/air-bag deployment, PDOF(Principal Direction of Force) by ΔV to be able to decide whether or not to adopt. also the event data is considered enough to vehicle damaged state, accident situation at the scene of the accident. and there is described in "all data should be examined in conjunction with other available physical evidence from the vehicle and scene" in the CDR(Crash Data Retrieval) report. Therefore many investigators have to decide whether or not to adopt after they consider sufficiently to above factors when they are the traffic accident analysis and investigate the causes of a accident on the adopted event data. In this paper, we report to traffic accident investigators notable points and analysis methods on the basis of thousands of cases and the results of one's own experiment in NFS(National Forensic Service).

• 자동차안전학회지
• /
• 제15권4호
• /
• pp.88-94
• /
• 2023

The seating postures of passengers in the automated driving vehicle are possible in atypical forms such as rear-facing and lying down. It is necessary to improve devices such as airbags and seat belts to protect occupants from injury in accidents of the automated driving vehicle, and collision safety evaluation tests must be newly developed. The purpose of this study is to define representative types of head-on collision accidents to develop collision standards for autonomous vehicles that take into account changes in driving behavior and occupants' postures. 150 frontal collision cases remained by filtering (accident videos, images, AIS 2+, passenger car, etc…) and random sampling from approximately 320,000 accidents claimed by a major insurance company over the past 5 years. The most frequent accident type is a head-on collision between a vehicle going straight and a vehicle turning left from the opposite side, accounting for 54.7% of all accidents, and most of these accidents occur in permissive left turns. The next most common frontal collision is the center-lane violation by drowsy driving and careless driving, accounting for 21.3% of the total. For the two types above, data such as vehicle speed, contact point/area, and PDOF at the moment of impact are obtained through accident reconstruction using PC-Crash. As a result, two types of autonomous vehicle crash safety test scenarios are proposed: (1) a frontal oblique collision test based on the accident types between a straight vehicle and a left-turning vehicle, and (2) a small overlap collision test based on the head-on accidents of center-lane violation.