  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.
