Research on the Detection Framework for Dangerous Riding Electric Scooters

E-scooters are eco-friendly and convenient, making their rental services highly popular. However, simultaneously, due to issues such as a surge in user numbers and a lack of user awareness about relevant traffic laws, related accident rates have increased tenfold in the last five years. As a result, dangerous riding of e-scooters is being presented as a new social issue. This study proposes a framework for detecting dangerously operating e-scooters in a fixed single-camera environment, which is cost-effective and conducive to accident detection. The proposed method uses object detection and tracking technology to identify people and e-scooters, simultaneously detecting multiple riders, helmet non-use, and sidewalk riding. For validation, it achieved excellent dangerous behavior detection performance in 17 diverse scenarios directly generated. Furthermore, compared to existing methods, it could detect more dangerous riding behaviors and provided detailed information, such as separately mapping dangerous riding results for each subject during multiple-rider situations. These results are expected to play a crucial role in enhancing urban traffic safety.