Construction of Collision-Type Prediction Models Based on Pre-crash Data for Advanced Driver Assistance Systems

Abstract

ABSTRACT: Collision-type prediction models based on pre-crash information are important because there is a relationship between collision type, avoidance operations, and occupant injuries. Thus, they can be applied to autonomous driving systems (ADS) or advanced driver assistance systems (ADAS) to prevent serious accidents or minimize damage during collisions. In this study, we investigated the application of collision-type prediction models based on several machine learning methods and compared their performance to determine the best model based on their f1 scores. The results revealed that the light gradient boosting machine (LGBM) model had a high f1 score that exceeded 0.92, which implied that it could potentially be used for ADS and ADAS applications. Furthermore, a brief analysis was performed on the ranking of various factors, which provided useful insight into the significance of several pre-crash factors and their distributions.