Conceptual Design of a Connected Vehicle Wrong-Way Driving Detection and Management System

Finley, Melisa D. (Melisa Dayle); Balke, Kevin N.; Rajbhandari, Rajat; Chrysler, Susan T.; Dobrovolny, Chiara Silvestri; Trout, Nada D.; Avery, Paul; Vickers, David; Mott, Cameron · 2016 · ROSA P / Texas A&M Transportation Institute

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Summary

This paper presents the conceptual design for a Connected Vehicle (CV) Wrong-Way Driving (WWD) Detection and Management System, developed by the Texas A&M Transportation Institute in cooperation with the Texas Department of Transportation and the Federal Highway Administration. The research addresses the persistent safety issue of wrong-way driving on controlled-access highways, which causes approximately 360 fatalities annually in the United States. While traditional Intelligent Transportation Systems (ITS) using sensors like radar and cameras have been deployed to detect errant drivers, these systems often lack real-time warning capabilities for right-way drivers and rely heavily on manual operator intervention. The project aimed to leverage CV technologies to automate detection, notify traffic management entities and law enforcement, and alert affected travelers to reduce crash severity. The methodology involved a comprehensive review of the state of practice regarding ITS and CV countermeasures, alongside an analysis of WWD crash trends in Texas from 2010 to 2014. The team conducted a needs assessment to identify user requirements and stakeholder impacts, including structured interviews with emergency service providers to determine integration protocols. Additionally, one-on-one surveys were administered to evaluate motorist comprehension of warning messages displayed on Dynamic Message Signs (DMS) and Roadside Alert (RSA) messages intended for CVs. Based on these inputs, the researchers developed a Concept of Operations, defined functional requirements, and created a high-level system architecture comprising detection, notification, verification, alert, and data warehouse modules. Key findings include the identification of specific operational scenarios, such as WWD events within Traffic Management Center coverage areas versus those relying solely on Vehicle-to-Vehicle communication. The study highlighted the necessity of integrating emergency service providers through preliminary connectivity concepts to streamline response protocols. The human factors analysis provided insights into effective message formatting for both DMS and CV interfaces, ensuring that warnings are clearly understood by motorists. The research also emphasized the importance of distinguishing between inadvertent and deliberate wrong-way driving to tailor appropriate interventions. The significance of this work lies in its provision of a foundational framework for implementing CV-based WWD mitigation systems. The authors recommend the development of an off-roadway proof-of-concept test bed to refine system components and operations before field deployment. This approach allows for the fine-tuning of detection algorithms and alert mechanisms in a controlled environment. Furthermore, the report calls for additional human factors studies to better understand motorist responses to RSA data elements. By establishing these design considerations and operational guidelines, the study supports the transition from manual, infrastructure-dependent WWD detection to automated, connected vehicle systems that can provide immediate, precise warnings to prevent fatal crashes.

Key finding

The research team recommended the development of a proof-of-concept test bed at an off-roadway location to test and fine-tune system components prior to installing them on an open roadway.

Methodology

mixed_methods

Provenance

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

Topics

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• v2x connected vehicle