MASH TL-6 Evaluation of a 62-in. Tall, Single-Slope Concrete Median Barrier

During the completion of the three-year MATC Smart Barrier project conducted at NTC's MwRSF, several state DOTs and the Ontario Good Roads Association indicated a strong interest in applying research findings and techniques from the initial study to state DOT datasets and connected and autonomous vehicle (CAV) wireless communication platforms. In particular, by using the MATC Smart Barrier final results, the Modified Discrete Road Curvature (MDRC) method, roads can be simply and accurately represented with much less data processing and storage needs than lidar-based maps, less robust connectivity requirements than geopositioning systems like real-time kinetic (RTK) Global Positioning System (GPS) systems, and fewer sensitivities to environmental conditions like rain, snow, fog, and darkness. By coordinating with U.S. and international agencies, MwRSF researchers intend to implement MATC research project findings into real-world prototypes and facilitate better adoption and integration of findings into state DOTs nationwide. Researchers will develop software to rapidly extract critical road data, standardize data format for ease of integration into state DOT and international road data formats, and develop a user guide for rapidly generating the road data matrix using MDRC method to represent real roadways. The method and software will be implemented to digitize selected road corridors.

The objective of this research effort is to develop software tools and user guides for implementing the findings from the three-year MATC Smart Barrier project into state data. As well, this project will standardize data formats for the road data matrix, allowing other researchers, industry, public agencies, and vehicle systems to use a standardize platform. The tools developed in this study will be showcased by mapping selected road corridors and verifying road data geospatially and graphically.

Current CAV research is significantly more advanced than the state-of-the-art a decade ago, but many limitations on vehicle autonomy and run-off-road (ROR) crash prevention still remain. Non-paved roads, adverse weather, and rural environments pose significant challenge for existing CAV systems. The MATC Smart Barrier research proposed a new complimentary paradigm which used simplified road representations, combinations of "fixed" road data and "dynamic" updates (including speed limits or lane closures) has the potential to significantly augment existing CAV guidance systems by providing external redundancy, but could be further used as a platform for future fully-autonomous driving. By carefully digitizing road coordinates and shapes and denoting vehicle position with respect to road geometries, better driver warning systems or vehicle autonomous corrections may be achieved in many environments, particularly for rural roads. Implementing this system and findings could reduce fatal rural road ROR crashes, resulting in approximately 5,000 lives saved per year.

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