E-scooter Crash Data Analysis towards E-scooter Automatic Emergency Braking System Design and Validation for Automated Vehicles

Diwas Thapa, Syed Mohammad Adil, Lingxi Li, Sabyasachee Mishra, Renran Tian, Stanley Y. P. Chien, Yaobin Chen, Rini Sherony

Abstract: Electric scooters (e-scooters) have become increasingly popular for intermodal transportation across major US cities, raising safety concerns for both motorists and non-motorists. To develop E-scooter AEB (Autonomous Emergency Braking) systems for intelligent vehicles, it is important to understand the association between e-scooter crash characteristics including facility type, crash severity, and motorist/non-motorist maneuvers. This paper investigates e-scooter crashes to map variable associations. The findings reveal that less severe crashes may be highly underreported. The most critical facilities for e-scooter safety are intersection crosswalks and travel lanes, with the former resulting in more severe injuries. Observing crash trajectories, right-turn crashes are found to be most common at intersections, while only a few intersection crashes result from left-turning vehicles. The findings highlight the need for dedicated safety design and policies for e-scooters, especially for vehicles at intersection crosswalks. Since data on e-scooter crashes is limited, future studies should focus on gathering larger samples from a wider geographic area to investigate and quantify predictor-crash relationships and develop diagnostic and predictive models using statistics and data-driven approaches.