2 research outputs found
A Wearable Data Collection System for Studying Micro-Level E-Scooter Behavior in Naturalistic Road Environment
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.Comment: Conference: Fast-zero'21, Kanazawa, Japan Date of publication: Sep
2021 Publisher: JSA
Enabling Autonomous Navigation for Affordable Scooters
Despite the technical success of existing assistive technologies, for example, electric wheelchairs and scooters, they are still far from effective enough in helping those in need navigate to their destinations in a hassle-free manner. In this paper, we propose to improve the safety and autonomy of navigation by designing a cutting-edge autonomous scooter, thus allowing people with mobility challenges to ambulate independently and safely in possibly unfamiliar surroundings. We focus on indoor navigation scenarios for the autonomous scooter where the current location, maps, and nearby obstacles are unknown. To achieve semi-LiDAR functionality, we leverage the gyros-based pose data to compensate the laser motion in real time and create synthetic mapping of simple environments with regular shapes and deep hallways. Laser range finders are suitable for long ranges with limited resolution. Stereo vision, on the other hand, provides 3D structural data of nearby complex objects. To achieve simultaneous fine-grained resolution and long range coverage in the mapping of cluttered and complex environments, we dynamically fuse the measurements from the stereo vision camera system, the synthetic laser scanner, and the LiDAR. We propose solutions to self-correct errors in data fusion and create a hybrid map to assist the scooter in achieving collision-free navigation in an indoor environment