# Evaluation of Infrastructure-based Warning System on Driving Behaviors-A Roundabout Study

arXiv: https://arxiv.org/abs/2312.03891 · HTML: https://mlsanity.com/paper/2312.03891/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 6 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [Real-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Cameras](https://mlsanity.com/paper/2206.09770/) (2022)
- [CEMFormer: Learning to Predict Driver Intentions from In-Cabin and External Cameras via Spatial-Temporal Transformers](https://mlsanity.com/paper/2305.07840/) (2023)
- [A Cooperative Control Framework for CAV Lane Change in a Mixed Traffic Environment](https://mlsanity.com/paper/2010.05439/) (2020)

## Built on by (most cited first, 1 of 1)

- [On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation](https://mlsanity.com/paper/2411.11913/) (2024)

## Abstract (arXiv)

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%.