Abstract
Automated Vehicles (AVs) are expected to dramatically reduce traffic accidents that have occurred when using human driving vehicles (HVs). However, despite the rapid development of AVs, accidents involving AVs can occur even in ideal situations. Therefore, in order to enhance their safety, “preventive design” for accidents is continuously required. Accordingly, the “preventive design” that prevents accidents in advance is continuously required to enhance the safety of AVs. Specially, black ice with characteristics that are difficult to identify with the naked eye—the main cause of major accidents in winter vehicles—is expected to cause serious injuries in the era of AVs, and measures are needed to prevent them. Therefore, this study presents a Convolutional Neural Network (CNN)-based black ice detection plan to prevent traffic accidents of AVs caused by black ice. Due to the characteristic of black ice that is formed only in a certain environment, we augmented image data and learned road environment images. Tests showed that the proposed CNN model detected black ice with 96% accuracy and reproducibility. It is expected that the CNN model for black ice detection proposed in this study will contribute to improving the safety of AVs and prevent black ice accidents in advance.
Highlights
As discussions on the fourth industrial revolution become more active, there is a movement to utilize big data, artificial intelligence, and 5G
This study is conducted in the following order: Section 2 discusses the research on the use of Convolutional Neural Networks (CNN) in the field of transportation and derives the differentiation of this research, while Seciton 3 sets up the CNN model learning environment for the detection of black ice
Data were collected via classification into four classes, and each class’s train, validation, and test data were set through pre-processing of split, padding, and augmentation
Summary
As discussions on the fourth industrial revolution become more active, there is a movement to utilize big data, artificial intelligence, and 5G. This study is conducted in the following order: Section 2 discusses the research on the use of Convolutional Neural Networks (CNN) in the field of transportation and derives the differentiation of this research, while Seciton 3 sets up the CNN model learning environment for the detection of black ice. Seciton 4 identifies and analyzes learning results through models, and Seciton 5 presents implications and future studies with a brief summary
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