# Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies
## 4 Experimental Results
### Experimental Tests Under Weather Conditions, Lighting Conditions, and Road Types  
The experimental results demonstrate that the model effectively recognizes obstacles encountered in electric wheelchair-driving environments. The proposed model detected key objects, including pedestrians, vehicles, and stationary items (e.g., benches and bicycles), on both roads and sidewalks, thereby underscoring its ability to provide swift and accurate obstacle detection. However, certain cases exhibited slightly lower confidence levels, especially for essential objects, such as pedestrians and stationary obstacles on sidewalks. This issue was more pronounced in environments in which multiple objects were present simultaneously. Improving pedestrian detection, which is a critical aspect of wheelchair safety, and increasing the recognition accuracy of stationary obstacles, such as benches and streetlights, remain key areas for improvement. To this end, we conducted additional experiments according to various weather conditions, lighting conditions, and road types, and they reflected the results in the paper as follows. (1) Additional Experiments Based on Lighting Conditions  
Looking at Figure 32, it can be observed that under nighttime lighting conditions, unlike during the daytime, the following characteristics are evident. In both the first and second images, vehicles were clearly detected, with confidence levels reaching up to 0.93. In particular, the second image demonstrates that vehicles in various angles and positions were accurately identified. As such, the system's high detection confidence even in low-light environments highlights its ability to prevent collisions in nighttime traffic conditions. This is particularly significant in areas prone to accidents, such as intersections or adjacent areas where electric wheelchairs may operate. (2) Additional Experiments Based on Road Type  
As shown in Figure 33, differences in the performance of the object detection system are observed depending on the road type, whether it is a sidewalk or a roadway. In the first image, vehicles and trucks located on the sidewalk were detected with confidence levels of 0.65 and 0.46, respectively. This demonstrates that even smaller objects can be detected in environments like sidewalks. Figure 32: Object detection performance under nighttime lighting conditions. In the second image, a vehicle on the roadway was detected with a high confidence level of 0.92, indicating that object detection tends to perform more clearly in roadway environments. In the third image, vehicles were accurately detected with a confidence level of 0.83, even in an alleyway with mixed shadows and bright sunlight. In conclusion, when the road type is a sidewalk, the relatively simple surroundings allow for the detection of various objects. On the other hand, on roadways, larger objects such as vehicles can be detected with high confidence. These results demonstrate the system's ability to perform effectively regardless of road type. Additional Experiments Based on Weather Conditions  
As shown in Figure 34, differences in the object detection system's performance are observed under varying weather conditions, such as clear and snowy weather. In the first and second images, taken during nighttime with no precipitation, vehicles and trucks were detected with confidence levels of up to 0.86 and 0.71, respectively. These results indicate that the system performs well even in low-light environments when the weather is clear. Figure 34: Additional experiments based on weather conditions. Figure 33: Additional experiments based on road type. In the third image, captured during a cloudy day, multiple objects, such as persons, skateboards, and benches, were detected. Although the confidence levels varied (e.g., people detected with confidence levels up to 0.69), the system successfully recognized a wide range of objects in an urban environment with diffused lighting conditions.