*2.3. Summary*

In summary, it was confirmed that various studies using black ice detection research and CNN in the transportation field are in progress. In addition, a study on CNN in the transportation sector has been conducted to detect the most important objects that make up road environment, such as pedestrians, vehicles, tra ffic signs, and road surfaces, and object detection using CNN shows a fast processing speed and high accuracy. In spite of such studies, it is expected that there is a limit to preventing black ice accidents in advance due to problems such as the installation of traditional black ice detection systems. Accordingly, this study proposes a method to detect black ice by identifying road conditions based on the CNN technique to prevent black ice accidents in AVs

### **3. Learning Environment Setting**

CNN is a type of AI that uses convolutional computation, which emerged in 1998 when Yann LeCun proposed the LeNet-5 [43]. CNN is one of the most popular methodologies in image analysis, with features that maintain and deliver spatial information on images by adding synthetic and pooling layers to existing Artificial Neural Networks (ANN) to understand dimensional characteristics. As we considered earlier, there are various studies using CNN in the transportation sector, but the study of black ice detection on the road has only thus far been conducted using other methodologies (sensors and optics) [10–15] other than research using AI. Black ice is reckoned to be a potential accident factor in the future era of AVs as it leads to large-scale collisions in winter due to features that are hard to distinguish with the naked eye. Accordingly, we will perform the detection of black ice by utilizing the CNN technique, which is considered to have excellent performance in object detection using images, rather than the traditional black ice detection methods.

The proposed learning environment of the CNN model for black ice detection consists largely of data collection and preprocessing, model design and the learning process. In this chapter, we set up the data collection, 1st preprocessing, and 2nd preprocessing, and the model was designed and learning undertaken (see Figure 1).

**Figure 1.** Learning Environment Setting Process.

### *3.1. Data Collecting and Preprocessing*

This chapter consists of data collection for learning black ice detection, 1st preprocessing, and 2nd preprocessing.
