A Wearable Data Collection System for Studying Micro-Level E-Scooter Behavior in Naturalistic Road Environment

TL;DR

This paper introduces a wearable data collection system using LiDAR, cameras, and GPS to study micro-level e-scooter behavior in naturalistic road environments, addressing safety and interaction challenges.

Contribution

It presents a novel integrated hardware and software system for continuous, accurate data collection of e-scooter movements and interactions in real-world settings.

Findings

System successfully collects continuous e-scooter data

Achieves high calibration accuracy for sensor synchronization

Enables detailed analysis of e-scooter behavior and interactions

Abstract

As one of the most popular micro-mobility options, e-scooters are spreading in hundreds of big cities and college towns in the US and worldwide. In the meantime, e-scooters are also posing new challenges to traffic safety. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than the pedestrains and bicyclists. These features make e-scooters challenging for human drivers, pedestrians, vehicle active safety modules, and self-driving modules to see and interact. To study this new mobility option and address e-scooter riders' and other road users' safety concerns, this paper proposes a wearable data collection system for investigating the micro-level e-Scooter motion behavior in a Naturalistic road environment. An e-Scooter-based data acquisition system has been developed by integrating LiDAR, cameras, and GPS using the robot operating system (ROS). Software frameworks are developed to support hardware interfaces, sensor operation, sensor synchronization, and data saving. The integrated system can collect data continuously for hours, meeting all the requirements including calibration accuracy and capability of collecting the vehicle and e-Scooter encountering data.