## 1 Introduction
### Previous Research  
Research on classifying vehicle types and colors while driving on the road has several applications, such as traffic management information systems [26] and intelligent transportation systems [20]. [PERSON] and [PERSON] [20] proposed a method to classify and analyze vehicle colors and types using a deep learning neural network. This method can be applied to Amber and Silver Alert emergency situations. Their proposed method was classified into six types of vehicles: trucks, buses, vans, SUVs, sedans, and motorcycles, and eight categories of vehicle colors: green, blue, black, white, gray, yellow, turquoise, and red. The dataset used for training included images of various types of vehicles, such as sedans, buses, SUVs, and trucks. However, in their method, pre-processing was applied to the images used for learning to improve image quality due to changes in lighting, haze, and excessive exposure to lighting. As a result, its real-time application to self-driving vehicles is limited. [PERSON] et al. [21] proposed a method for vehicle detection and color classification that can be used for intelligent traffic management and support in vehicular criminal investigations. They pre-trained a lightweight MobileNet-v2 CNN [27] and reduced the network complexity by replacing Darknet-5 on the YOLO v3 backbone network. They trained a total of 24 color datasets and proposed a color classification method. Their research aimed to detect vehicles and classify their colors from images captured by fixed cameras installed on the upper part of the road, not in a driving environment. The detected vehicles in their research were relatively large and had the characteristics of driving only in a certain area. They presented relatively good color classification results by experimenting with images obtained from a high-resolution camera. [PERSON] et al. [28] proposed a method for classifying pedestrians and vehicle types at night using thermal imaging cameras. To classify vehicle types in the special environment of night, only the features of vehicle outlines obtained from thermal images were extracted and Histogram of Oriented Gradients(HoG) features and SVM classifier were used to classify types of trucks and sedans. [PERSON] et al. [29] proposed a method that used the YOLO v4 DNN to detect and classify vehicles as coupes, pickups, sedans, SUVs, and vans. However, the method had the disadvantage of requiring a significant amount of learning time due to the acquisition of side images of vehicles parked on the road and learning the deep learning algorithm of YOLO v4 being learned without the use of a transfer learning. Their proposed method classified vehicle types from side images of parked vehicles rather than driving vehicles, which allowed for more information about the side parts of the vehicle. However, our method differs from this by classifying vehicle types using rear and some side images of the moving vehicle. [PERSON] et al. [19] proposed a method to classify vehicles into seven colors under different lighting conditions by color correction. To reduce the risk of misclassifying the same vehicle color into different colors due to light scattering and diffuse reflection on the vehicle surface, they designed a tree-based color classifier. The classifier was binary classified into achromatic and chromatic, and then classified into detailed colors using color features. They presented the method to improve the accuracy of vehicle color classification, even when the vehicles have different colors and are acquired under varying lighting conditions. The method involved extracting only the vehicle regions by removing the background from the image sequence, and classifying colors using a tree-based classification of representative colors of vehicles with the same color. [PERSON] et al. [30] proposed a method for classifying vehicle types in CNN-based rear vehicle images. The detected vehicle region was normalized to a fixed size of 32 \(\times\) 32 pixels before classification.