Analysis of accidents involving vehicles with automatic emergency call systems (D-Call Net) and further utilization

Masayuki Shirakawa･Toru Kiuchi (Institute for Traffic Accident Research and Data Analysis)

One effective way to reduce traffic accident fatalities is through an automatic emergency notification system (D-Call Net). The number of vehicles compatible with the system is increasing year by year and is continuing to expand. We will analyze the actual accident situations involving response vehicles and present their effectiveness and areas where further expansion of application is desired. We will also introduce examples of successful use of D-Call Net from actual accident investigations.

Study on actual situations of D-Call Net by matching automatic notification data with ITARDA Macro data

Toru Kiuchi (ITARDA)･Testuya Nishimoto (Nihon University)･Nobuo Saito･Ichiro Ando (JAPAN MAYDAY SERVICE)･Eiko Kagesawa･Mayu Ishii (ITARDA)

In recent years, the number of automatic notifications has dramatically increased due to the spread of vehicles equipped with D-Call Net. The authors were able to obtain notification data from three new OEMs in addition to the existing one. Therefore, we conducted a new effect study by matching the 2023 data with ITARDA macro data. This time, we focused on the fatal and serious injury rate and analyzed the actual situation.

In-depth Study on Pedestrian and Cyclist Accidents Transported to Emergency Hospital, and Verification of Injury Prediction Algorithms

Tetsuya Nishimoto (Nihon University)･Tomokazu Motomura (Nippon Medical School)･Nagaoka Yasushi (Toyota Motor)

A study was conducted on approximately 200 cases of pedestrians and cyclists versus motor vehicle accidents where victims were transported to an emergency hospital. The results of analyses concerning collision speed, injured body regions, AIS and so on. Furthermore, injury severity was predicted using our injury prediction algorithms for pedestrian and cyclist, and consideration was given to automatic emergency notification systems for vulnerable road users.

Artificial Intelligence as a Catalyst for Next-Generation Emergency Call Systems

Hamma Tadjine･Amine Kaddache (IAV)･Isao Hasegawa (IAV Japan)

Advanced emergency call systems currently transmit basic vehicle data during accidents. To enhance passenger safety, new features focus on improved data collection aligned with NENA2 and NG-AACN standards, considering in-vehicle architecture and communication protocols. The goal is to provide precise information about occupants and surroundings for better rescue operations. AI integration boosts efficiency and effectiveness, optimizing eCall systems through rapid data analysis and prioritizing calls by severity. AI enhances design and operational capabilities, enabling automated data processing and swift emergency assessment. Predictive analytics foresee potential emergencies, supporting proactive measures and strategic resource allocation for high-risk scenarios.

In-depth investigation of traffic accidents with medical and engineering network by ITARDA

Sentaro Terakado･Takehiro Tsuji･Tatsuya Ito (Institute for Traffic Accident Research and Data Analysis)･kazuaki Shinohara (Ohta General Hospital Foundation)･Toru Kiuchi (Institute for Traffic Accident Research and Data Analysis)

ITARDA has been conducted in-depth investigation of traffic accidents with medical and engineering network in order to obtain basic data that contribute to mitigate injuries caused by traffic accidents since 2016. This investigation is consortium by ITARDA, Hospital and vehicle manufacturers, traffic accidents in around Tokyo were investigated. Occurrence mechanism of injury due to traffic accidents was discussed by consortium members including medical professionals and engineers based on medical images and vehicle damage. In this report provides the results of in-depth investigation of traffic accidents.