# Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies
## 3 System Architecture, Design, and Implementation
### Enhancing Driving Safety by Forward Obstacle Detection
Optimized with TensorRT on AGX Orin hardware, YOLOv8 demonstrated exceptional performance in small object detection and inference speed, meeting the stringent requirements of forward obstacle detection in real-world scenarios. The experimental results confirmed YOLOv8's superiority over other models, making it a highly effective solution for enhancing situational awareness in electric wheelchairs. Various object detection models were evaluated to optimize the performance of the forward situational awareness system. Due to the need for high processing speed and accuracy in real-time awareness, the assessment focused on one-stage models, including YOLOv8, SSD, EfficientDet, RetinaNet, and the two-stage model Faster R-CNN. As shown in Figure 7, the object detection models were evaluated in an experimental setup using a camera and an embedded board. Figure 6: One-stage object detection algorithm exploited on our platform. Figure 7: Experimental setup for object detection using a camera and embedded board. The efficiency and accuracy of object detection are important. An object detector is an object detection that performs image recognition tasks by taking an image as input and then predicting bounding boxes and class probabilities for each object in the image. This is because, today, object detection technology plays a key role in enhancing driving safety in our research. Currently, state-of-the-art real-time object detectors are mainly based on YOLO-like convolutional one-stage object detection algorithms. For this reason, in this study, we also pursued near-real-time fast performance in the process of performing the object detection algorithm using YOLO, a one-stage algorithm. After evaluating the performance and efficiency of the real-time object detection models, we selected YOLOv8 as the final model. As discussed earlier, two-stage models, such as Faster R-CNN, offer high accuracy through a refined multi-step process. However, their computational complexity makes them unsuitable for real-time applications. In contrast, one-stage models, such as YOLO, process an entire image in a single step, providing the speed required for real-time environments while maintaining a balance between accuracy and efficiency. Models such as SSD, EfficientDet, and RetinaNet were also considered. However, they did not meet the requirements of this study. Despite its speed advantages, SSD was excluded due to its low accuracy in detecting small objects. Although EfficientDet offers exceptional computational efficiency, its speed and small object detection performance are inferior to that of YOLOv8. Similarly, while RetinaNet effectively detects objects of various sizes, its slower processing speed makes it unsuitable for real-time applications. To ensure an informed decision, we reviewed the performance data from Ultralytics and conducted comparative experiments with YOLOv5, a widely used model in various applications. The experimental results indicate that YOLOv8 significantly outperformed YOLOv5 in terms of both accuracy and inference speed. YOLOv8 excels at small object detection and offers faster processing, making it suitable for real-time applications such as forward obstacle detection for electric wheelchairs. Additionally, YOLOv8 is highly compatible with modern hardware and operates efficiently on low-spec embedded systems, making it versatile across various environments. These advantages led to the selection of YOLOv8 as the optimal model for this study. A comprehensive analysis of YOLOv8's performance, including a comparative evaluation with YOLOv5, is provided in the performance evaluation section.