**1. Introduction**

As discussions on the fourth industrial revolution become more active, there is a movement to utilize big data, artificial intelligence, and 5G. Among them, Automated Vehicles (AVs), a collection of various technologies, are attracting attention in the transportation field. AVs are expected to bring effects such as improving mobility for the vulnerable and reducing traffic congestion costs and are expected to minimize human and material losses in terms of preventing traffic accidents caused by driver negligence [1,2]. Currently, various companies such as Google, NVIDIA, and Tesla are developing and experimenting with AV systems, and each country is reorganizing its institutional foundation to prepare for the commercialization of AVs. Despite these efforts, however, traffic accidents continue to occur in autonomous driving situations, and the social acceptability of AVs has emerged due to Uber's pedestrian deaths in 2018 [3–5]. In order to solve these problems fundamentally, Germany and the United States have issued an ethics guideline for AVs [6,7]. The guidelines specify the need to develop principles to cope with dilemma situations, along with information on the preventive design of the AVs to avoid accidents. Preventive design of AVs is an issue about risk managemen<sup>t</sup> that can occur in a realistic driving environment, changing from passive safety systems to active safety systems research [8]. In addition, recently, there has been a change in the tendency toward preventing accidents themselves by learning all the accident situations related to AVs [9]. While various preventive design studies are being carried out, there is a lack of research on preventing black ice accidents, which are the main cause of large-scale tra ffic accidents in winter. Black ice is a thin ice film formed on the road by combining rain and snow with pollutants such as dust, which is likely to lead to fatal accidents because it is di fficult to identify with the naked eye. As black ice is considered to be a potential accident factor even in the era of commercialization of AVs, it is expected that technologies that can detect it in advance, and thus prevent accidents, will be required. Therefore, we will adopt the Convolutional Neural Network method, which is known to detect object's images most e ffectively, to present measures for preventing AV-related black ice accidents in the study.

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 di fferentiation 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.

## **2. Literature Review**

In this chapter, we consider existing detection methods for black ice and studies using CNN in the transportation field to derive the di fferentiation of this study.

### *2.1. Black Ice Detection Methods*

Methods for detecting black ice include sensors [10–12], sound waves [13,14], and light sources [15]. Habib Tabatabai et al. (2017) [10] conducted a study to detect black ice, ice and water in roads and bridges using sensors embedded in concrete. In this study, a sensor that detects the road surface condition was proposed through the change of electrical resistance between stainless steel columns inside concrete. As a result of conducting experiments under various surface conditions, it was suggested that the proposed sensor can e ffectively detect the road condition, thereby preventing various accidents. Nuerasimuguli ALIMASI et al. (2012) [11] conducted a study to develop a black ice detector consisting of an optical sensor and an infrared thermometer. The study was conducted in Route 39 around Sekihoku Pass, Hokkaido, and was conducted on a total of six road conditions (dry, wet, "sherbet", compact snow, glossy compacted snow, black ice) and di ffuse reflection and reflection. As a result of the experiment, black ice had a large specular reflection ( *Rs*) and a small di ffuse reflection (*RD*) was measured, resulting in a low ( *Rs*/*RD*) value. Youngis E. Abdalla et al. (2017) [12] proposed a system for detecting black ice using Kinect. The types of ice (Soft Ice, Wet Snow, Hard Ice, Black Ice) were classified and the thickness and volume of the ice were measured using Kinect. Experiments have shown that the types of ice formed in the range of 0.82 m to 1.52 m from the camera can be distinguished, and the error rate of measured thickness and volume is very low, suggesting that black ice can be detected by utilizing Kinect. Xinxu Ma et al. (2020) [15] studied a black ice detection method using a three-wavelength non-contact optical technology. The study conducted an experiment to distinguish dry, wet, black ice, ice and snowy conditions using three wavelengths (1310 nm, 1430 nm, 1550 nm). As a result of the experiment, it was confirmed that black ice was detected through the reflectance of each wavelength, and it was suggested that it can be used as basic data for the development of equipment to detect road conditions.

### *2.2. Deep Learning Applications to Intelligent Transportation*

Artificial Intelligence (AI) methodologies are currently being used in various fields, and CNN studies using image data for the detection of vehicles and pedestrians, detection of tra ffic signs, and detection of road surface are actively carried out in the transportation field.

First of all, for vehicle and pedestrian detection, studies using AlexNet [16,17], VGG (Visual Geometry Group) 16 [18], Mask R-CNN [19], and Faster R-CNN [20,21] existed, and a comparative analysis of the performance of Faster R-CNN and YOLO (You Only Look Once) [22,23]. Lele Xie et al. (2018) [24] conducted a vehicle license plate detection study at various angles using CNN-based

Multi-Directive YOLO (MD-YOLO). The study proposed an ALMD-YOLO structure combining CNN and MD-YOLO, and compared the performance of various models (ALMD-YOLO, Faster R-CNN, SSD (Single Shot multibox Detector), MD-YOLO, etc.) and found that the newly proposed ALMD-YOLO had the best performance. It also suggested that the simple structure of the model reduced the computational time and that a high-performance multi-way license plate detection model could be established. Ye Yu et al. (2018) [25] proposed a CNN-based Feature Fusion based Car Model Classification Net (FF-CMNET) for the precise classification of vehicle models. The above study utilized FF-CMNET, which combines UpNet to extract the upper features of the car's frontal image and DownNet to extract the lower features. Experiments have shown performance better than traditional CNN methodologies (AlexNet, GoogLeNet, and Network in Network (NIN)) in terms of extracting the car's fine features. M. H. Putra et al. (2018) [26] conducted a study using YOLO to detect people and cars. Unlike the traditional YOLO structure, the above study proposed a modified YOLO structure using seven convolutional layers and compared their performance. As a result of the study, the modified YOLO's 11 × 11 grid cells model had a lower mAP compared to the traditional YOLO model, but had better processing speed. In addition, tests with actual images showed that small-sized people and cars could be extracted.

Second, in the case of tra ffic sign detection, there have been many studies using the basic CNN structure [27–30], Mask R-CNN [31,32], and Faster R-CNN [33,34]. Rongqiang Qian et al. (2016) [35] conducted a study using Fast R-CNN to recognize tra ffic signs on road surfaces. To enhance the performance of the model, the experiment was conducted by utilizing MDERs (Maximally Stable Extremal Regions) and EdgeBoxes algorithms in the object recognition process. The results of the experiment showed that the Recall rate was improved, with an average precision of 85.58%. Alexander Shustanov and Yakimov, P. (2017) [36] conducted a CNN model design study for real-time tra ffic sign recognition. The study used modified Generalized Hough Transform (GHT) and CNN, with 99.94% accuracy. It was also confirmed that the proposed algorithm could process high-definition images in real time and accurately recognize tra ffic signs farther away than similar tra ffic sign recognition systems. Lee, H.S. and Kim, K. (2018) [37] conducted a study using CNN to recognize the boundaries of tra ffic signs. They designed CNN based on SSD architecture, and unlike previous studies, they proposed a method of estimating the positions of signs and converting them into boundary estimates. Experiments have confirmed that various types of tra ffic sign boundaries can be detected quickly.

Finally, studies of road surface detection were reviewed to identify road surface conditions and to detect road cracks. Juan Carrillo et al. (2020) [38] and Guangyuan Pan et al. (2020) [39] are both studies that identify road surface conditions. The above studies divided the data into three and four classes and compared the performance of the CNN model. Studies found that up to 91% accuracy was derived, and CNN showed excellent performance in road surface identification. In the road crack detection study, Janpreet Singh et al. (2018) [40] conducted a study using Mask R-CNN to detect road damage in images taken with smartphones. The data utilized 9053 road damage images taken with smartphones, and the CNN's structure utilized Mask R-CNN. Experiments have confirmed that road damage is detected e ffectively, showing high accuracy and 0.1 s processing speed. Zheng Tong et al. (2018) [41] conducted a study to classify the length of asphalt cracks using DCNN (Deep Convolutional neural networks). Data collection was conducted in various places and weather conditions, and the data were divided into eight classes from 0 cm to 8 cm in 1 cm increments. As a result of the experiment, the accuracy was 94.36% and the maximum length error was 1 cm, and it was suggested that the length of the crack can be classified as well as the existence of a simple crack. Baoxian Li et al. (2020) [42] conducted a study using CNN to classify road crack types. Road cracks were classified into a total of five types (non-crack, transverse crack, longitude crack, block crack, alligator crack) and four models were designed using the basic CNN structure. As a result of the experiment, the accuracy of the four models designed was 94% or more. It was also confirmed that CNN, which has a 7 × 7 size of the reactive field, was the best choice for crack detection.
