Safely and Effectively Communicating Non-Connected Vehicle Information to Connected Vehicles through Field- and Driving-Simulator-Based Research

Noyce, David A.; Nassereddine, Hiba; Santiago-Chaparro, Kelvin R.; Riehl, Jon · 2019 · ROSA P / Safety Research Using Simulation (SAFER-SIM) University Transportation Center

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Summary

This research addresses the safety limitations of connected vehicle (CV) technology during the transition period before widespread market penetration. Because CVs rely on vehicle-to-vehicle communication, their safety benefits are currently limited by the prevalence of non-connected vehicles (non-CVs). The study specifically investigates how infrastructure-based radar detection systems can identify non-CV red-light runners—vehicles that may be obscured from in-vehicle sensors due to blind spots—and communicate this hazard to CV drivers. The primary objectives were to evaluate the feasibility of mapping radar data to standard CV safety messages and to assess the effectiveness of advanced warning systems in preventing collisions. The researchers conducted a two-part study. First, they performed a feasibility analysis using field tests to demonstrate that radar-based vehicle detection (RVD) systems could track non-CV position and speed, process this data via an intersection processor, and transmit Intersection Collision Avoidance (ICA) messages to CVs via dedicated short-range communications. Second, they utilized a full-scale driving simulator at the University of Wisconsin–Madison to evaluate driver response to these warnings. Participants were exposed to imminent collision scenarios involving red-light-running vehicles emerging from blind spots. The experimental design compared a control group, which received warnings at the stop bar, against treatment groups receiving visual head-up display (HUD) and auditory warnings at 50, 100, and 150 feet before the stop bar. To emulate real-world conditions, participants also performed a secondary task involving pedestrian detection. The results indicated that earlier warnings significantly improved driver reaction times and safety outcomes. The average reaction time for the control group (warning at the stop bar) was 0.05 seconds, whereas reaction times for the 50, 100, and 150-foot warning groups were 0.94, 1.22, and 1.31 seconds, respectively. Statistical analysis confirmed significant differences between these groups. Furthermore, drivers reduced their speed for longer durations as the warning distance increased, averaging 2.15 seconds for the stop bar group and 3.15 seconds for the 150-foot group. The likelihood of coming to a complete stop also increased with earlier warnings, rising from 29.73% at the stop bar to 47.7% at 150 feet. Despite the added cognitive load of the warning system, participants maintained a 70.38% detection rate for pedestrians in the secondary task, suggesting the HUD interface did not cause excessive distraction. The study concludes that infrastructure-based detection can effectively supplement CV capabilities by providing critical safety information about non-connected vehicles. By leveraging existing radar infrastructure to generate ICA messages, transportation systems can mitigate blind-spot hazards and red-light running risks even with low CV market penetration. The findings support the implementation of advanced warning systems that provide drivers with sufficient time to react, thereby enhancing intersection safety during the transitional phase of autonomous and connected vehicle adoption.

Key finding

Drivers reacted significantly faster and were more likely to come to a complete stop when warning systems were activated at greater distances from the stop bar, with reaction times increasing from 0.05 seconds at the stop bar to 1.31 seconds at 150 feet prior.

Methodology

simulator

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• ehmi external hmi
• vru facing ehmi
• hud ar windshield
• v2x connected vehicle
• in vehicle coaching
• visual

Information type

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• Empirical Findings: behavioral performance data
• Methodological Resource: tool software, measurement protocol