Development and Testing of a Prototype Connected Vehicle Wrong-Way Driving Detection and Management System
archive: archived pipeline: cataloged verified
Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)
Summary
This report details the development and testing of a prototype Connected Vehicle (CV) Wrong-Way Driving (WWD) detection and management system, conducted by the Texas A&M Transportation Institute in cooperation with the Texas Department of Transportation and the Federal Highway Administration. The research addresses the critical safety issue of wrong-way driving by leveraging CV technology to detect errant vehicles, notify traffic management agencies and law enforcement, and alert right-way drivers. The study represents Phase II of a larger project, aiming to refine system components and operations in a controlled, off-roadway environment before potential field deployment. The methodology involved building a prototype system at the Texas A&M RELLIS campus using both an integrated architecture via the Lonestar® ActiveITS software and a standalone roadside unit (RSU) configuration. The hardware included onboard units (OBUs) with Dedicated Short-Range Communication (DSRC) capabilities, RSUs, portable changeable message signs, and traditional non-DSRC detectors. The software architecture featured new subsystems: a Wrong-Way Monitor for detecting vehicles based on heading and position data from Basic Safety Messages, and a Connected Vehicle Subsystem for managing message parsing and transmission. Validation testing covered four scenarios: CV-enabled wrong-way vehicles, message propagation between vehicles, detection via non-DSRC infrastructure sensors, and manual entry for reported events. Additionally, human factors studies, including task analysis, structured interviews, and tablet-based surveys, were conducted to determine the information needs and message preferences of right-way drivers. The results demonstrated high system reliability, with the Lonestar® software receiving and processing 99.8 percent of transmitted Basic Safety Messages from CV-enabled wrong-way vehicles. The system successfully detected wrong-way events, generated appropriate roadside alerts, and propagated warnings through intermediate CVs to extend communication range. Human factors findings indicated that right-way drivers preferred specific information types and message timings to make informed decisions. The validation confirmed that the prototype could effectively integrate traditional detection methods with CV technology to provide comprehensive coverage. The significance of this work lies in its contribution to the advancement of intelligent transportation systems for wrong-way mitigation. The successful prototype demonstrates the feasibility of using CV technology to improve detection accuracy and notification speed compared to traditional methods. The report recommends a model field deployment on State Highway 47 to further test the system in real-world conditions. This next step aims to enhance the ability of public agencies and law enforcement to respond to wrong-way incidents and to alert motorists more effectively, ultimately improving roadway safety as connected vehicle infrastructure expands.
Key finding
The prototype system processed 99.8 percent of basic safety messages from connected wrong-way vehicles and successfully demonstrated message propagation between vehicles to extend communication range.
Methodology
mixed_methods
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Applied Guidance: design guidelines
- Methodological Resource: validation psychometrics, tool software