Evaluation of Infrastructure-based Warning System on Driving Behaviors-A
  Roundabout Study
Cong Zhang Chi Tian Tianfang Han Hang Li Yiheng Feng Yunfeng Chen Robert W. Proctor Jiansong Zhang
Smart intersections have the potential to improve road safety with sensing,
communication, and edge computing technologies. Perception sensors installed at
a smart intersection can monitor the traffic environment in real time and send
infrastructure-based warnings to nearby travelers through V2X communication.
This paper investigated how infrastructure-based warnings can influence driving
behaviors and improve roundabout safety through a driving-simulator study - a
challenging driving scenario for human drivers. A co-simulation platform
integrating Simulation of Urban Mobility (SUMO) and Webots was developed to
serve as the driving simulator. A real-world roundabout in Ann Arbor, Michigan
was built in the co-simulation platform as the study area, and the merging
scenarios were investigated. 36 participants were recruited and asked to
navigate the roundabout under three danger levels (e.g., low, medium, high) and
three collision warning designs (e.g., no warning, warning issued 1 second in
advance, warning issued 2 seconds in advance). Results indicated that advanced
warnings can significantly enhance safety by minimizing potential risks
compared to scenarios without warnings. Earlier warnings enabled smoother
driver responses and reduced abrupt decelerations. In addition, a personalized
intention prediction model was developed to predict drivers' stop-or-go
decisions when the warning is displayed. Among all tested machine learning
models, the XGBoost model achieved the highest prediction accuracy with a
precision rate of 95.56% and a recall rate of 97.73%.