10 research outputs found
A Large-Scale SUMO-Based Emulation Platform
A hardware-in-the-loop simulation platform for emulating large-scale intelligent transportation systems is presented. The platform embeds a real vehicle into SUMO, a microscopic road traffic simulation package. Emulations, consisting of the real vehicle, and potentially thousands of simulated vehicles, are run in real time. The platform provides an opportunity for real drivers to gain a feel of being in a large-scale, connected vehicle scenario. Various applications of the platform are presented
Enabling the Evaluation of Driver Physiology Via Vehicle Dynamics
Driving is a daily routine for many individuals across the globe. This paper
presents the configuration and methodologies used to transform a vehicle into a
connected ecosystem capable of assessing driver physiology. We integrated an
array of commercial sensors from the automotive and digital health sectors
along with driver inputs from the vehicle itself. This amalgamation of sensors
allows for meticulous recording of the external conditions and driving
maneuvers. These data streams are processed to extract key parameters,
providing insights into driver behavior in relation to their external
environment and illuminating vital physiological responses. This innovative
driver evaluation system holds the potential to amplify road safety. Moreover,
when paired with data from conventional health settings, it may enhance early
detection of health-related complications.Comment: 7 pages, 11 figures, 2023 IEEE International Conference on Digital
Health (ICDH
A Distributed and Privacy-Aware Speed Advisory System for Optimising Conventional and Electric Vehicles Networks
One of the key ideas to make Intelligent Transportation
Systems (ITS) work effectively is to deploy advanced
communication and cooperative control technologies among the
vehicles and road infrastructures. In this spirit, we propose a
consensus based distributed speed advisory system that optimally
determines a recommended common speed for a given area in
order that the group emissions, or group battery consumptions,
are minimised. Our algorithms achieve this in a privacy-aware
manner; namely, individual vehicles do not reveal in-vehicle information
to other vehicles or to infrastructure. Mathematical proofs
are given to prove the convergence of the algorithm, SUMO
simulations are given to illustrate the efficacy of the algorithm,
and hardware-in-the-loop tests involving real vehicles are given
to illustrate user acceptability and ease of the deployment