## 1 Introduction  
Vehicles have evolved beyond mere means of transportation and are now integral to daily life. Self-driving technology enables drivers to reach their destinations without focusing on driving [1; 2; 3]. However, reaching fully autonomous driving, where no driver intervention is needed, will take more time [4]. Autonomous driving depends on safe driving, and various safety technologies are constantly being developed and implemented in autonomous vehicles. To support safe driving of self-driving vehicles, safety support technologies are constantly being applied to vehicles but not road infrastructure [5; 6; 7]. Technologies for vehicle safety support are also being applied to road signs, lanes, traffic lights, and tunnels [7; 8]. Three elements are required to drive a vehicle: the road, vehicle, and driver. However, the adoption of technologies in vehicles to enhance driver focus for safe driving remains significantly low [8]. For example, there are scenarios in which one cannot concentrate on driving because of the use of a smartphone, watching multimedia, or talking to a passenger, which can lead to traffic accidents. Recently, technologies related to safe driving of autonomous vehicles have been released and installed as commercial products in both general and autonomous vehicles. Typical examples are lane departure prevention forward collision avoidance and surround view monitoring systems [9; 10; 11]. Autonomous driving support systems focus on detecting vehicles on the road and providing safety support technologies that recognize the distance, driving direction, trajectory, and speed of the vehicle [12; 13]. If the system can also identify and classify the types and colors of the detected vehicles, it will be possible to provide safer driving technology support based on more information. For example, when an autonomous vehicle is driving on a four-lane road and has to change lanes, it is crucial to select the safest lane and adjust the driving route accordingly. Lane selection is typically based on the presence of vehicles in adjacent lanes, both in front of and behind the driving vehicle [14]. If a large truck or bus is driving in three lanes, although it does not pose a direct safety threat while driving, it will change from two lanes to one lane rather than from two lanes to three lanes [15]. Therefore, vehicle type detection and classification processes are required [16; 17; 18]. In addition, accurately classifying the color of a vehicle can improve its detection and tracking capabilities. The proposed method classifies vehicle types by tracking the bounding boxes of the vehicles detected in three consecutive frames. Previous studies have proposed methods for detecting vehicles and determining their types from a single input image [19; 20; 21]. However, in dynamic road environments like highways, as explored in this paper, real-time simultaneous identification of a vehicle's type and color using a black-box camera I not feasible [22; 23]. In this study, the You Only Look Once version 4 (YOLO v4) deep learning neural network (DNN) [24], which is based on the pre-trained ResNet-50 convolutional neural network (CNN) [25], was used to detect vehicles in successive frames. Subsequently, the type and color of the detected vehicle were classified using a ResNet-50 CNN. The vehicle types were classified into four types (sedan, SUV, truck, and bus) according to the vehicle size for safe driving, and the vehicle colors were classified into eight types (white, black, silver, yellow, red, orange, blue, and green). The proposed method detects vehicles driving ahead in a dynamic environment, such as a highway, and classifies the types and colors of vehicles in real time. The proposed method provides several advantages when using ResNet-50 CNN. First, it is composed of deep neural network layers and exhibits an excellent performance in learning complex patterns and features, which helps to detect various vehicle features and shapes.