# Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies
## 4 Experimental Results
### Forward Obstacle Detection Performance  
We chose forward situational awareness as the object detection technique due to its exceptional balance between speed and accuracy. A detailed performance evaluation using metrics such as mAP50, mAP50-95, precision, and recall confirmed YOLOv8 suitability for forward situational awareness. The experimental results demonstrated that YOLOv8 achieved a mAP50 score of 74%, which indicates reliability in detecting small objects. In addition, with a mAP50-95 score of 50.6%, YOLOv8 exhibited consistent performance across objects of varying sizes and positions. The model achieved a precision of 80.8%, indicating an excellent ability to reduce false detections, and a recall of 63.3%, ensuring adequate sensitivity for real-time detection. The train/box_loss graph represents the loss incurred by the model when predicting the object locations during training. As training progressed, the loss decreased, indicating that the model's ability to accurately predict object positions was improved. The train/box_loss graph in Figure 19 shows a consistent decrease in loss as training progressed. The sharp decline in loss during the initial epochs indicates the rapid learning of the basics of predicting object locations, while the gradual reduction in later stages indicates continued refinement of the model's localization accuracy. This stable training progression highlights YOLOv8's effectiveness in achieving precise obstacle detection, which meets the objectives of this study. The train/cls_loss graph in Figure 20 illustrates the model's consistent improvement in object classification during training. The sharp decline in the initial loss, followed by a steady reduction, indicates effective learning and refinement. This trend highlights YOLOv8's capability to distinguish between various obstacle types, making it well-suited  
Figure 19: Train/box loss (bounding box loss for training data). for real-time applications, such as electric wheelchair navigation, where accurately categorizing different obstacle types is essential. As shown in Figure 21, Distribution Focal Loss (DFL) evaluates the model's accuracy in predicting object locations and sizes. The consistent decrease in loss during training indicates improved accuracy in predicting object boundaries. This capability is particularly critical for detecting small obstacles or objects in complex backgrounds, thereby ensuring safer navigation in scenarios where such obstacles pose safety risks. The Metrics/Precision graph in Figure 22 demonstrates YOLOv8's effectiveness in identifying true positives while minimizing false positives. The consistently high precision throughout the training process demonstrates the model's reliability in accurately detecting obstacles without generating excessive false alerts. This capability is essential for providing reliable obstacle warnings to users. Figure 21: Train/DFL loss (DFL loss for training data). Figure 20: Train/Cls loss (class loss for training data). The Metrics/Recall graph in Figure 23 evaluates the model's sensitivity in detecting obstacles. Although recall remained high throughout the training process, minor fluctuations indicated potential areas for optimization, particularly for detecting small or partially occluded objects. Future work could focus on enhancing recall to improve the detection of such challenging obstacles. The Val/Box Loss graph in Figure 24 indicates the model's effectiveness in generalizing bounding box predictions on unseen data. The reduction in the loss in the validation data highlights YOLOv8's strong generalizability, ensuring reliable performance in real-world scenarios. This robustness is critical for reliable obstacle detection in different environments. The Val/Cls Loss graph in Figure 25 illustrates the model's accuracy in classifying objects in the validation dataset.