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Recently, e-scooter-involved crashes have increased significantly but littleinformation is available about the behaviors of on-road e-scooter riders. Mostexisting e-scooter crash research was based on retrospectively descriptivemedia reports, emergency room patient records, and crash reports. This paperpresents a naturalistic driving study with a focus on e-scooter and vehicleencounters. The goal is to quantitatively measure the behaviors of e-scooterriders in different encounters to help facilitate crash scenario modeling,baseline behavior modeling, and the potential future development of in-vehiclemitigation algorithms. The data was collected using an instrumented vehicle andan e-scooter rider wearable system, respectively. A three-step data analysisprocess is developed. First, semi-automatic data labeling extracts e-scooterrider images and non-rider human images in similar environments to train ane-scooter-rider classifier. Then, a multi-step scene reconstruction pipelinegenerates vehicle and e-scooter trajectories in all encounters. The final stepis to model e-scooter rider behaviors and e-scooter-vehicle encounterscenarios. A total of 500 vehicle to e-scooter interactions are analyzed. Thevariables pertaining to the same are also discussed in this paper.

Risk assessment and mitigation of e-scooter crashes with naturalistic driving data