Estimation of Driver's Awareness of Obstacles while Riding an Electric Scooter with Anomaly Detection Model

Abstract

In this study, we address the development of a driver assistance system for electric kickboards aimed at preventing falls from steps, such as sidewalks, encountered when maneuvering to avoid pedestrians. Central to our investigation is the differentiation between instances where the driver is cognizant of the step and those where the step is unnoticed, positing that assistance is only requisite in the latter scenario. To facilitate this, we collect and analyze driving data capturing vehicle momentum under both conditions to compile a dataset. This dataset serves as the foundation for training an anomaly detection-based machine learning model, designed to estimate the driver's awareness of steps. Our approach offers a novel contribution to enhancing safety features in electric kickboard mobility, leveraging data-driven insights to anticipate and mitigate potential fall risks associated with urban navigation.