3 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
Automatic Building and Labeling of HD Maps with Deep Learning
In a world where autonomous driving cars are becoming increasingly more
common, creating an adequate infrastructure for this new technology is
essential. This includes building and labeling high-definition (HD) maps
accurately and efficiently. Today, the process of creating HD maps requires a
lot of human input, which takes time and is prone to errors. In this paper, we
propose a novel method capable of generating labelled HD maps from raw sensor
data. We implemented and tested our methods on several urban scenarios using
data collected from our test vehicle. The results show that the pro-posed deep
learning based method can produce highly accurate HD maps. This approach speeds
up the process of building and labeling HD maps, which can make meaningful
contribution to the deployment of autonomous vehicle.Comment: Accepted by IAAI202