# Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies
## 4 Experimental Results
### Situational Obstacle Detection Performance Analysis  
To evaluate the real-time obstacle detection performance of the system in electric wheelchair driving environments, experiments were conducted using images captured in various road and intersection scenarios. The analysis evaluated the proposed system's ability to recognize obstacles, pedestrians, vehicles, and stationary objects commonly encountered during driving. This assessment measured the proposed system's object recognition accuracy in real-world situations faced by electric wheelchair users.  
Figure 29 shows the obstacles detected on the road ahead, including a cyclist, a bench, and parked vehicles. The YOLOv8 model accurately identified various obstacles, including pedestrians, stationary objects (e.g., a bench), and vehicles, with confidence levels ranging from 0.26 to 0.44, although vehicles were detected at a slightly lower confidence level. These results highlight the model's ability to identify critical objects, such as stationary obstacles or vehicles that electric wheelchair users must avoid on sidewalks or pathways. Promptly detecting obstacles on the driving path and alerting users enhances navigation safety. However, further research is required to improve the reliability of detecting dynamic objects, such as moving vehicles.  
Figure 30 illustrates the results from an intersection crowded with pedestrians and vehicles, a common scenario for electric wheelchair users. The model detected pedestrians with high confidence (0.61 to 0.77), successfully identifying most individuals despite their large number and varied positions. This capability enables wheelchair users to anticipate pedestrian movements for safe navigation.  
Figure 28: Metrics/mAP50-95 (mean average precision from IoU 0.5 to 0.95).  
Figure 29: Obstacle detection on a roadway.  
Vehicles at the intersection were detected with a lower confidence level of 0.26 than pedestrians, which varied depending on road conditions, vehicle size, and position. For wheelchair users near roads, prompt vehicle identification and appropriate responses are essential for maintaining safety, particularly in assessing vehicle positions and maintaining a safe distance when crossing roads.  
Since intersections are the environments where pedestrians and vehicles coexist, assessing the model's performance in such scenarios is vital. The results indicate that the proposed model effectively detected multiple objects even in crowded situations, demonstrating its potential to enhance the safe navigation of electric wheelchairs in complex urban environments.  
Figure 31 shows the detection results for vehicles, pedestrians, and traffic lights on the highway. Vehicles were detected with confidence levels up to 0.89, while pedestrians and traffic lights were also identified appropriately. The high confidence level in vehicle detection emphasizes the system's ability to prevent collisions on roadways, especially near intersections or adjacent areas where electric wheelchairs may operate. Rapid and accurate vehicle detection ensures safe navigation, and traffic light detection improves safety when crossing intersections. Pedestrians, which represent a crucial detection target on roads and intersections, may require additional training to improve detection performance under diverse conditions.  
Figure 31: Obstacle detection on a highway.  
Figure 30: Obstacle detection in a crowded pedestrian road.