DriveOhio is conducting a rural Automated Driving System (ADS) project, focusing on 32 of Ohio’s Appalachian counties. Advanced Mobility Solutions (AMS) joins the Transportation Research Center, Bosch, AutonomouStuff, EASE Logistics, Ohio University, and the University of Cincinnati as a member of the consortium supporting the project.
The project aims to identify roadway challenges for automated vehicles in rural areas which are magnified due to moving from shaded areas under tree canopies to bright sunlight, as well as limited sight distances around curves and over hills on two-lane roads and divided highways.
“Automated driving systems are expected to transform roadway safety in the future, and the data collected with this project will be used to refine the technology to maximize its potential,” said DriveOhio Executive Director Preeti Choudhary. “Innovative partnerships like this one are critical to advancing the safe integration of automated vehicle technologies in Ohio and across the nation.”
An important component of the project is data collection of all performance characteristics for the automated trucks, including tire health data to help researchers understand how the autonomous vehicles perform in real-world situations.
AMS president Joe Cole states, “We are proud to partner with DriveOhio and the consortium members as Ohio continues to lead the nation in forging a safe plan for autonomous driving, realizing the importance of monitoring and maintaining safe operating conditions for tires in this environment.”
AMS will deploy their Internal Tire Sensor (ITS) technology which includes a sensor mounted inside of every tire on each of the test vehicles. The sensors collect tire health data such as pressure, temperature, and tread depth. Most importantly, the data is transmitted to the cloud data analytics engine which sends real-time alerts and notifications for conditions such as rapid air loss, low pressure, heat buildup, and low tread depth all of which can lead to catastrophic tire failure. Additionally, the AMS system uses Artificial Intelligence (AI) and Machine Learning (ML) to identify tire performance by make, model, and position on the vehicle. These data insights will be important to better understand tire related implications of autonomous driving in rural areas.
The project will help define technology needs and limitations as well as inform the safe scaling of future vehicle automation deployments in the U.S. For more information visit https://drive.ohio.gov/programs/av-cv/rural-automated-driving-systems
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Mitchell Langford
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