E Scooter Rider

E-scooter rider behavior and detection are emerging research areas driven by the increasing prevalence of e-scooters and associated safety concerns. Current research focuses on improving rider safety through interventions like post-ride feedback to encourage prosocial behavior and developing advanced computer vision systems, often employing YOLO object detection models and other convolutional neural networks, to accurately detect e-scooter riders in complex urban environments, even under partial occlusion. These efforts aim to mitigate e-scooter-related accidents and inform the development of safer urban transportation systems and autonomous vehicle technologies. The resulting datasets and algorithms are contributing to a more comprehensive understanding of e-scooter rider dynamics and improving the safety of both e-scooter riders and other road users.