Honda's self-driving tech tackles Ohio's road woes
In a nationwide first, Honda has partnered with DriveOhio to deploy its Proactive Roadway Maintenance System across 3,000 miles of central and southeastern Ohio. Equipped with advanced vision and LiDAR sensors, the system scans for signs of deterioration, such as damaged road signs, guardrails, and potholes.
ODOT drivers put the vehicle's capabilities to the test, driving through varied urban and rural landscapes in different weather conditions and at various times of day. The system's findings were reviewed via smart dashboards developed by Honda and Parsons, with the University of Cincinnati playing a key role in integrating the sensors and damage detection feature.
The vehicles' onboard data was processed using Edge AI models before being transmitted to Honda's cloud platform. This generated work orders for ODOT's maintenance teams based on priority levels. Notably, the system achieved accuracy rates of 99% for damaged signs, 93% for guardrails, and 89% for potholes.
Honda claims its technology can also detect high-severity shoulder drop-offs that are difficult to spot in a routine inspection. Moreover, it demonstrated reliability in measuring road roughness. If scaled up nationwide, the automated system could save ODOT approximately $4.5 million annually.
As the project progresses, Honda plans to expand the prototype and eventually integrate similar technology into its production vehicles. This will enable customers to share anonymous detection data, further enhancing road safety.
In a nationwide first, Honda has partnered with DriveOhio to deploy its Proactive Roadway Maintenance System across 3,000 miles of central and southeastern Ohio. Equipped with advanced vision and LiDAR sensors, the system scans for signs of deterioration, such as damaged road signs, guardrails, and potholes.
ODOT drivers put the vehicle's capabilities to the test, driving through varied urban and rural landscapes in different weather conditions and at various times of day. The system's findings were reviewed via smart dashboards developed by Honda and Parsons, with the University of Cincinnati playing a key role in integrating the sensors and damage detection feature.
The vehicles' onboard data was processed using Edge AI models before being transmitted to Honda's cloud platform. This generated work orders for ODOT's maintenance teams based on priority levels. Notably, the system achieved accuracy rates of 99% for damaged signs, 93% for guardrails, and 89% for potholes.
Honda claims its technology can also detect high-severity shoulder drop-offs that are difficult to spot in a routine inspection. Moreover, it demonstrated reliability in measuring road roughness. If scaled up nationwide, the automated system could save ODOT approximately $4.5 million annually.
As the project progresses, Honda plans to expand the prototype and eventually integrate similar technology into its production vehicles. This will enable customers to share anonymous detection data, further enhancing road safety.