## 3 Model Design  
### Scenario Description  
The scenario considered in this study is shown in Figure 1. Car A is traveling on a road (hill road or level street) or highway when the intelligent sensor on the car's dashcam (as depicted in the \"Dashcam video screen\" inset in Figure 1) detects, and alerts the driver to, the road conditions ahead--for example, the presence of a speed limit sign, a rockfall, pothole, or car crash. The system can be integrated with existing speed limit warning apps to achieve a dual reminder function. When a speed limit sign is detected, the system alerts the driver to slow down so that they can focus on other road conditions or their driving route and prevent a car crash. When rockfalls, potholes, or car crashes are detected, the information is transferred through the Internet of Vehicles (IoV) to the drivers of the vehicles behind (i.e., Car B and Car C), prompting them to slow down to prevent a secondary car crash or further congestion. In addition, the information is simultaneously transferred, via fifth generation (5G) base stations, to the competent authorities (police, fire brigade, paramedics) firsthand, so that they can arrive at the car crash scene quickly to manage it. In this study, we focused on the detection of speed limit signs, rockfalls, potholes, and car crashes as well as the classification of the detected objects. The topic regarding the information transmission through wireless communication technologies could be further explored in future studies (the transmission technologies designed by [PERSON] [62] and [PERSON] and [PERSON] [63] could be revised and applied here).  
Figure 1: Schematic showing the object detection scenario.  
In this study, the objects to be detected were first divided into speed limit signs (Class A) and rockfalls, potholes, and car crashes (Class B). Because speeding is strongly associated with car crashes, we focused more on Class A object detection by subdividing it into nine subcategories based on speed limits commonly seen on highways or normal roads--30 km/h (Class A_30), 40 km/h (Class A_40), 50 km/h (Class A_50), 60 km/h (Class A_60), 70 km/h (Class A_70), 80 km/h (Class A_80), 90 km/h (Class A_90), 100 km/h (Class A_100), and 110 km/h (Class A_110). This reminded drivers not to exceed the speed limit and to pay attention to the road conditions ahead or the driving route as a means of preventing a speeding offense or a car crash.  
In addition, we determined a method for distinguishing between different speed limit signs. In exceptional cases, two speed limit signs appear at the same time, which normally happens on expressways and freeways where vehicles of different sizes are subjected to different speed limits. Our solution for resolving this problem was as follows: when the moving vehicle was a small car, the model settings were set to detect the higher speed limit; conversely, when the moving vehicle was a large automobile, the model settings were set to detect the lower speed limit. The decision-making flow diagram is shown in Figure 2. The decision-making flow diagram is continuously implemented after the vehicle has begun to run and until it has come to a halt. Consider the example of choosing between 90 km/h and 110 km/h when both speed limit signs are detected at the same time. The 90 km/h speed limit would be applied to the larger automobiles, and the 110 km/h speed limit would be applied to the small cars.  
Figure 2: Decision-making flow diagram where two speed limit signs are detected at the same time.