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arXiv · 2212.12660

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

Avinash Prabu·Zhengming Zhang·Renran Tian·Stanley Chien·Lingxi Li·Yaobin Chen·Rini Sherony
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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.

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Avinash Prabu, Zhengming Zhang, Renran Tian, Stanley Chien, Lingxi Li, Yaobin Chen, Rini Sherony. 2023-01-15. Risk assessment and mitigation of e-scooter crashes with naturalistic driving data. https://arxiv.org/abs/2212.12660

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