Investigation of ΔV Calculation Method Considering Vehicle Rotation Behavior in Side Impact Accidents

Noboru Tanase･Shizue Katsumata･Takahiro Ando･Yasushi Nagaoka (Toyota Motor)

The calculation of the fatality and severe injury rate in D-Call Net (AACN) uses pseudo ΔV as an indicator of the severity of collision accidents. The calculation of pseudo ΔV is based on the assumption that "the impact acts on the center of gravity" and "the vehicle does not rotate." Under this assumption, approximately the same ΔV occurs in any non-deformed part of the vehicle body. Therefore, when comparing pseudo ΔV with vehicle data, the ΔV from the airbag ECU has been used. However, in accidents that deviate from this assumption, a certain degree of error may occur, potentially leading to inaccurate estimates of the fatality and severe injury rate.

Research on automatic detection of vehicle damage using deep learnig

Tetsuya Nishimoto･Kazuhiro Kubota･Kento Nakao (Nihon University)･Tomokazu Motomura (Nippon Medical School)･Martion Elsegood･Sam Doecke･Giulio Ponte (The University of Adelaide)

We have developed an image recognition algorithm that uses deep learning to determine the extent of vehicle damage based on traffic accidents that have occurred in Japan. To verify its effectiveness, we applied it to 700 EDR data and vehicle damage images collected in South Australia, and examined its practicality. As a result, we found that it can instantly recognise the extent of damage with a 70% accuracy rate.

How does pre-crash environment affect injury risk? Injury prediction and analysis based on graph neural network

JUNHAO WEI･Yusuke Miyazaki (Institute of Science Tokyo)･Fusako Sato (JARI)

Injury severity in vehicle crashes is influenced by various factors within the pre-crash environment including environment, vehicle and driver attributes, together with other pre-crash attributes. To investigates the relationships among the factors to identify key determinants of injury outcomes, this study employs graph neural network to model complex interactions and dependencies from police-reported tabular database. The analysis reveals critical contributors to injury severity, uncovering relationships among variables in pre-crash environment. These findings provide actionable insights for enhancing traffic safety and developing effective injury prevention strategies.

Investigation of Severe Injury Probability Prediction Models by Body Parts Through Decision Tree-Based Machine Learning Approach

YIMENG MEI･Haruhiro Fukushima･Yusuke Miyazaki (Institute of Science Tokyo)･Fusako Sato (JARI)

Traffic accidents result in countless economic losses and casualties worldwide annually, making reducing casualties in road accidents always been a hot topic in research. In this context, quick and accurate prediction of occupant injury severity in traffic accidents helps emergency services respond more effectively. However, previous research effects have mainly concentrated on predicting overall injury severity rather than looking at injuries to specific body parts, which limits the precision of injury assessment and targeted emergency response. In this study, we aim to develop specific Random Forest-based models to predict injury severity for different body parts, including the head, face, neck, thorax, abdomen, spine, and limbs. These models facilitate the analysis of correlations between various accident risk factors and body part-specific injuries. Furthermore, they enable emergency services to implement more precise and targeted response strategies after accidents happened.