Design of Crash Alert System to Prevent Motorcycle-Car Door Collision Using Artificial Intelligence
TL;DR
Experimental results demonstrate that the YOLOv12s object detection model achieved strong performance in predicting car door states, with precision, recall, and mean average precision (mAP) values of 85.5%, 84.0%, and 88.8%, respectively.
Abstract
Collisions between motorcycles and opening car doors are common causes of motorcycle-related accidents. To avoid these collisions, this study proposed a new crash alert system embedded in motorcycles to warn motorcyclists of opening car doors ahead, using artificial intelligence and a matching algorithm. The proposed method involved two stages: (1) training a deep learning model to detect car door states and (2) applying a matching process to eliminate incorrect opening door predictions to improve object detection model performance. Experimental results demonstrate that the YOLOv12s object detection model achieved strong performance in predicting car door states, with precision, recall, and mean average precision (mAP) values of 85.5%, 84.0%, and 88.8%, respectively. Validation on the test dataset further confirmed the model’s effectiveness, yielding precision and recall values of 86.8% and 83.6%, respectively. Moreover, integrating the matching algorithm improved precision to 92.0%. These findings highlight the high feasibility of the proposed method for real-world applications.