Development of AACN Algorithm for Pedestrian and Cyclist


The purpose of this study was to establish an injury prediction algorithm for an Advanced Automatic Collision Notification (AACN) system for vulnerable road users such as the pedestrian and bicycle users. The injury prediction algorithm was based on two independent samples of ITARDA macro data, one of which was used for training and the other as for validation. The AACN algorithm was developed using the Japanese large scale accident database by a logistic regression modeling technique. The Risk factors associated with severe injury for pedestrians and cyclists were travel speed, the frontal shape of striking vehicle, pedestrian and cyclist age, type of road and pedestrian and cyclist behavior. Validation of both AACN algorithms were verified using ROC analysis. The results indicate that for a 10% rate of under triage, the threshold values are 8.8% and 2.9% for pedestrians and cyclists respectively.ITARDAマクロデータを用いて歩行者では24 万件,自転車では58万件の対四輪車事故データからAACNのための歩行者および自転車乗員保護のためのアルゴリズムを構築した.構築したアルゴリズムはモデル構築に用いていないITARDAマクロデータとITARDAミクロデータにより検証し,事故実態に符合するアルゴリズムであることを示した.


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  • Accession Number: 01666784
  • Record Type: Publication
  • Source Agency: Japan Science and Technology Agency (JST)
  • Files: TRIS, JSTAGE
  • Created Date: Jan 31 2018 3:18PM