## 4 Discussion and Conclusions  
This paper proposes a method for autonomous vehicles to detect and classify vehicles ahead based on their type and color in order to select the optimal lane. The proposed method extracts feature from input images using a transition learning model as the base network and uses the YOLO v4 object detector to generate the vehicle detector. To classify the type and color of the vehicle within the detected bounding box, the final layer of the Resnet-50 CNN was modified, and a classifier was generated. The proposed method, tested in a road driving environment, achieved approximately 93.2% accuracy in detecting vehicle and classifying their type and color. It only identified the rear area of the vehicle as a correct vehicle detection and classified the detection of vehicles in the opposite lane as a false detection. Thus, the overall vehicle detection was relatively low. However, the accuracy of the vehicle type and color classification was high. However, this method has limitations and cannot be used under low light conditions or at night. Future research will explore applying this method infrared and thermal imaging sensors.  
The study proposes the following points for enhancing autonomous vehicle safety based on its method:  
Firstly, we must determine the type of vehicle being driven when changing lanes on high-speed roads. Vehicle types can be determined by size and can be used to select the optimal and safest driving lane. Because the experimental environment in this paper only considers vehicle types, we must integrate a driving vehicle management system that can manage vehicles driving on various roads in a cloud database.  
Secondly, using the color information of moving vehicles can increase vehicle identification and tracking accuracy. Although vehicle color can only be used during the day, it can identify vehicles for tracking purposes. To classify vehicle colors, a method that simplifies the classification of colors by grouping them is required.  
Finally, we must use a significant amount of vehicle data to determine the type and color of the vehicle ahead accurately. This information can support the safe driving of self-driving vehicles, therefore, driving data must be collected continuously. Self-driving vehicles require increased driver convenience, improved vehicle safety, enhanced driver assistance systems, and an efficient traffic flow. Therefore, national road safety authorities should prioritize research and development, particularly in new safe driving technologies that enhance the safety of self-driving vehicles.  
His work was supported by the Regulatory Sandbox Convergence New Product Certification Technology Development Project funded by the Ministry of Trade, Industry and Energy (20016800, 2021), the Research Project funded by the Korea Internet & Security Agency (KISA 2022-0279, 2022), and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1F1A106890011).  
Not applicable.  
The data supporting the conclusions of this study are accessible from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The author declares no conflicts of interest.