Results of Ohio’s autonomous driving tests show challenges and advancements

(WKBN) – The results are in from testing on Ohio’s roads of autonomous vehicles and semi-trucks.

A $7.5 million grant from the U.S. Department of Transportation was awarded to Ohio for the $13.4 million Rural Automated Driving Systems project. The data collected, some of which was analyzed by Youngstown State University students, will help with future technology for automated vehicles.

Two semi-trucks were tested and three vehicles. EASE Logistics was a partner in the study that provided two automated semi-trucks that drove together using platooning technology, meaning they followed each other using automated speed and spacing controls.

The driver in the lead truck controlled the speed and the following vehicle automatically responded to the lead vehicle’s movement. With no input from the second driver, the technology mimicked the speed of the lead vehicle, with precisely matched acceleration and braking.

The truck traveled 44,000 miles throughout Ohio, with 11,000 miles in platooning mode.

“The implementation of semi-automated trucks holds immense potential for enhancing roadway safety, optimizing transportation efficiency, and significantly reducing emissions across the U.S.,” said Peter Coratola, Jr., president and CEO of EASE. “We’re pleased with the results and believe these findings will be critical to delivering innovative and practical solutions that address the real-world challenges faced by supply chain managers today.”

EASE officials added that while automated driving systems have the potential to dramatically improve roadway safety, data shows that these systems must be refined to maximize that potential in rural areas.

Three autonomous passenger cars were tested on some of Ohio’s divided highways and rural two-lane roads in Athens and Vinton counties.

The cars operated at a higher level of autonomy than the semi-trucks and relied on high-definition route models. They were also equipped with external cameras and sensors.

The most common disengagement of the autonomy features involved a loss of GPS or cellular service, object detection, and the behavior of surrounding traffic or pedestrians.

Since the software required the models to be created in advance, the driver was not able to make adjustments to how the vehicle operated in real-time based on conditions like heavy traffic, weather, or work zones. Human driving behaviors are typically adjusted for these factors, so disengaging the technology and taking over manually was the only way to adapt to surrounding traffic behavior.

“From a safety driver’s perspective for autonomous vehicles, it took patience and time to adapt to the vehicle that would slow down on its own for curves and come to a stop at lights. The systems would always operate on the most efficient slow-down path, even though a driver might brake earlier,” said Dr. Jay Wilhelm, associate professor of Mechanical Engineering at Ohio University. “The disengagements by drivers were sometimes due to us humans applying more safety than needed. As time went on, we learned to trust the machine more and as a result, less disengagements occurred and more data was gathered.”

The passenger vehicles made 331 trips, covering 5,000 miles, with the majority of trips experiencing disengagement of the technology.

Students at YSU analyzed the data pulled from parameters such as vehicle position, speed, steering, acceleration, driving environment, traffic signal and object identification. The analytics was part of a year-long course at the university.

Ohio University was involved in the testing of the technology for the study.

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