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

TL;DR

This study uses naturalistic driving data to analyze e-scooter rider behaviors and vehicle encounters, aiming to improve crash modeling and develop mitigation strategies.

Contribution

It introduces a novel naturalistic data collection and analysis method for e-scooter crash scenarios, focusing on rider behaviors and encounter modeling.

Findings

Analyzed 500 vehicle-e-scooter interactions.

Developed a semi-automatic data labeling and scene reconstruction pipeline.

Provided insights into e-scooter rider behaviors in on-road encounters.

Abstract

Recently, e-scooter-involved crashes have increased significantly but little information is available about the behaviors of on-road e-scooter riders. Most existing e-scooter crash research was based on retrospectively descriptive media reports, emergency room patient records, and crash reports. This paper presents a naturalistic driving study with a focus on e-scooter and vehicle encounters. The goal is to quantitatively measure the behaviors of e-scooter riders in different encounters to help facilitate crash scenario modeling, baseline behavior modeling, and the potential future development of in-vehicle mitigation algorithms. The data was collected using an instrumented vehicle and an e-scooter rider wearable system, respectively. A three-step data analysis process is developed. First, semi-automatic data labeling extracts e-scooter rider images and non-rider human images in similar environments to train an e-scooter-rider classifier. Then, a multi-step scene reconstruction pipeline generates vehicle and e-scooter trajectories in all encounters. The final step is to model e-scooter rider behaviors and e-scooter-vehicle encounter scenarios. A total of 500 vehicle to e-scooter interactions are analyzed. The variables pertaining to the same are also discussed in this paper.