(2.28 MB) Abstract
The lack of knowledge of the micromobility regulations by e-scooter users is an important factor behind some of the accidents involving these vehicles. We present two modules that can increase the safety of the users and pedestrians: First, a computer vision model that analyses the video feed captured with a smartphone attached to the e-scooter, and predicts in real-time the type of lane in which the user is riding. This knowledge is used by an application which combines this information with GSNN location information and a database of mobility regulations, and informs the user when he/she is not complying with these regulations. Second, an accident detection system, using the smartphone accelerometer, that detects if there is a fall during the riding, so that the app can contact the authorities to determine the appropriate response. The experimental results show excellent results for both modules.
