Transportation
Cameras, AI and Sensors Bring New Tools for Road Inspection

Ohio DOT is testing a combination of tech tools in vehicles provided by Honda.
High-resolution dashcam images and other on-vehicle tools are gaining traction with state transportation departments as they use them to survey road assets such as guardrails, signage and striping to make more informed maintenance decisions.
Bentley Systems announced Feb. 5 that the Alabama Dept. of Transportation (ALDOT) has started using Blyncsy, which crowdsources high-resolution dash camera imagery from vehicles and applies artificial intelligence to analyze roadway conditions.
A majority of DOTs—more than 80%—have deployed or are considering adoption of Blyncsy, says Mark Pittman, Bentley’s director of transportation AI. Last year, Hawaii Dept. of Transportation provided 1,000 of the cameras to drivers for its Eyes on the Road program. “They have a significant shortage of inspectors,” says Pittman. “By using Blyncsy, their guardrail inspection time has dropped to every 12 hours” in a consistent manner, despite the islands’ diverse and often remote geographies.
Blyncsy’s AI models achieve 97% accuracy, providing the reliable data foundation required for precise financial planning, according to Bentley.
“To strengthen our performance-based budgeting, we need consistent, quantified data to produce condition assessments across all districts,” said Morgan Musick, ALDOT assistant maintenance management engineer, in a press release. “This technology helps to give us an objective snapshot of our roadway network, enabling us to adjust budgets based on actual asset conditions and ensure funding goes to appropriate maintenance activities.”
A Partnership in Ohio
Blyncsy is one of the tools being used by the Ohio Dept. of Transportation (ODOT) in a pilot project with the University of Cincinnati that also uses LIDAR and other sensors on two vehicles provided by Honda.
“Ohio DOT has 49,000 lane miles to maintain,” says Jodie Bare, project manager with Parsons, the systems integrator. “They have to inspect critical assets every two weeks. It’s two people getting into a car, one with a clipboard.” The duo must face urban traffic or remote rural treks, then input the data into multiple systems, she notes.
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Data from the various tools identifies potential issues such as potholes, pavement roughness, and striping and sign-age defects. These conditions are displayed on intuitive dashboards and translated into maintenance tickets with supporting imagery. AI further optimized workflows by grouping maintenance activities based on geofenced proximity, according to Parsons.
The two vehicles were tested in three counties in two to three-week stints, says Nicholas Hegemier, ODOT managing director of infrastructure and technology. “Inspectors in urban counties, having to focus on driving, spent a lot of time going out and looking at one thing—say potholes. Then they would go out again looking for guardrail damage.” With the combined system, “they were able to cut that driving down to one instance.”
The initial two-year pilot finished last fall. Because the vehicles only stayed in a county for a short time, “there were minimal up-front benefits—but we uncovered what they could be,” says Hegemier. The next phase will test them for a full year, one in a rural county and one in an urban one.
“We did have some inconsistencies” in the first phase, he says. “Something resembled a pothole but wasn’t. We hope for better representation and more characteristics like size and depth.” ODOT may eventually emulate the Hawaii DOT in crowdsourcing data in real time.
Hegemier notes the partnership with Honda. “Over the past 100 years, the [vehicle manufacturers] have made cars to drive on roads, but never really talked to DOTs.” But as connected vehicles have come into play, “this is a culmination of all those [subsequent] discussions.”



