**5. Conclusions**

This study conducted a study using a CNN to detect black ice that is di fficult to judge visually in order to prevent black ice accidents in AVs. 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. Unlike the DCNN model, the CNN model proposed in this study was designed to be relatively simple but showed an excellent performance with an accuracy of about 96%. This suggests that it is more e ffective to optimize the neural network depth according to the object to be detected rather than to detect black ice by increasing the amount of computation through a complex neural network model. In addition, in this study, a neural network was designed and learned through GRAYSCALE as a feature of black ice mainly formed at dawn, but it was found that some specific classes were confused due to the loss of light characteristics. Accordingly, we plan to conduct research on neural network design that is more optimized for black ice detection by utilizing RGB images in the future. Additionally, since the data were collected through Google Image Search, only images detected close to the object are classified. Accordingly, we plan to construct a CNN model applicable to various situations by setting the distance and angle to the object to be detected in various ways [48–50] in the future.

This study is significant in that black ice, which is deemed a potential risk factor even in the era of AVs, was detected using AI, not sensors and wavelengths. It is expected that this will prevent black ice accidents of AVs and will be used as basic data for future convergence research.

**Author Contributions:** Conceptualization, M.K. and K.H.; Data curation, H.L.; Formal analysis, H.L.; Methodology, M.K.; Project administration, M.K. and J.S.; Software, H.L.; Supervision, K.H.; Visualization, H.L.; Writing—original draft, H.L., M.K. and K.H.; Writing—review and editing, M.K. and K.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2020R1F1A106988411).

**Conflicts of Interest:** The authors declare no conflict of interest.
