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For Preventative Automated Driving System (PADS): Traffic Accident Context Analysis Based on Deep Neural Networks by Kang, Minhee; Song, Jaein; Hwang, Keeyeon is a Engineering article available to read on EtoBox.

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Automated Vehicles (AVs) are under development to reduce traffic accidents to a great extent. Therefore, safety will play a pivotal role to determine their social acceptability. Despite the fast development of AVs technologies, related accidents can occur even in an ideal environment. Therefore, measures to prevent traffic accidents in advance are essential. This study implemented a traffic accident context analysis based on the Deep Neural Network (DNNs) technique to design a Preventive Automated Driving System (PADS). The DNN-based analysis reveals that when a traffic accident occurs, the offender’s injury can be predicted with 85% accuracy and the victim’s case with 67%. In addition, to find out factors that decide the degree of injury to the offender and victim, a random forest analysis was implemented. The vehicle type and speed were identified as the most important factors to decide the degree of injury of the offender, while the importance for the victim is ordered by speed, time of day, vehicle type, and day of the week. The PADS proposed in this study is expected not only to contribute to improve the safety of AVs, but to prevent accidents in advance.

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Author

Kang, Minhee; Song, Jaein; Hwang, Keeyeon

Publisher

MDPI AG; Multidisciplinary Digital Publishing Institute (MDPI); Basel: MDPI AG, 2012- (ISSN 1754-1778)

Published

2020

Language

EN

Field

Engineering (Physical Sciences)