Driving data from multi-human-in-the-loop simulation experiments

Freeway ramp merging involves conflict of vehicle movements that may lead
to traffic bottlenecks or accidents. Thanks to advances in connected and
automated vehicle (CAV) technology, a number of efficient ramp merging
strategies have been developed. However, most of the existing CAV-based
ramp merging strategies assume that all the vehicles are CAVs or do not
differentiate vehicle type (i.e., passenger cars vs. heavy-duty trucks).
In this study, we propose a decentralized cooperative ramp merging
application for connected vehicles (both connected trucks and connected
cars) in a mixed-traffic environment. In addition, we develop a
multi-human-in-the-loop (MHuiL) simulation platform that integrates SUMO
traffic simulator with two game engine-based driving simulators, allowing
us to investigate the interactions between two human drivers under various
traffic scenarios. The case study shows that the decentralized cooperative
ramp merging application, which provides speed guidance to the connected
vehicles involved in ramp merging, helps increase the time headways of the
involved vehicles and smooths their speed profiles. With the speed
guidance, the median minimum time headway for the yielding car on the
mainline increases by 57%. Also, its speed variation decreases by 17%
while the speed variation of the merging truck from the on-ramp decreases
by 19%. These results demonstrate the potential for the proposed
application to improve the safety and efficiency of ramp merging for
heavy-duty trucks, which will be particularly useful at on-ramps with
relatively short merging lanes. The experiments conducted also validate
the effectiveness of the developed MHuiL platform for human factor
research.