Development of a Micromobility Riding Evaluation Platform for an Indoor Riding Lane Based on Multi-View Overhead Video Integration.

This study developed a simple and scalable e-scooter riding evaluation platform that reduces on-site implementation effort by relying primarily on image analysis. The platform supports repeated riding trials under consistent conditions in an indoor environment. By combining the areas covered by two drones, the system recorded an entire long and narrow indoor riding lane. A pretrained YOLO model was fine-tuned to construct a rider detection model adapted to the experimental environment. ORB-based image registration and trajectory integration then transformed the riding trajectories obtained from the two cameras into a common coordinate system. Riding speed, riding duration, and the radius of curvature of the two curves were calculated. The results revealed differences among subjects in speed variation, stability across riding trials, and turning characteristics, including stable low-speed riding, sustained high-speed riding, and deceleration before turning. For most subjects, the inter-camera junction discrepancy was within 10 cm, indicating general internal consistency of trajectory integration under the experimental conditions. These results suggest that the proposed system can serve as a video-based platform for quantitatively evaluating observable riding behavior, including riding trajectory and speed characteristics, in an indoor riding lane.