Abstract
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.
Highlights
Amid an active discussion of the Fourth Industrial Revolution, Automated Vehicles (AVs) are expected to play an important role in leading the Fourth Industrial Revolution
Deep Neural Networks (DNNs) are capable of modeling complex non-linear relationships, such as common artificial neural networks, with the ability to express basic elements in hierarchical configurations and the added layers to converge the characteristics of lower layers
Automated Vehicles are under the spotlight as an alternative to diminishing traffic accidents
Summary
Amid an active discussion of the Fourth Industrial Revolution, Automated Vehicles (AVs) are expected to play an important role in leading the Fourth Industrial Revolution. AVs are defined as vehicles capable of navigating, controlling, and avoiding risk partly or totally without human assistance [1]. Human intervention is minimized from SAE level 3 and driverless driving is possible at level 5. With such features as driving safety improvement, increase in convenience and mobility [3,4], AVs are highly evaluated as key future mobility of reducing traffic accidents. It is mainly attributed to AVs traffic accidents arisen during the test driving by Google, Uber, etc.
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