Virginia Tech study to explore autonomous driving systems

The research is funded by a $7.5 million grant from the U.S. DOT

April 29, 2020
Virginia Tech study to explore autonomous driving systems
Image: Virginia Tech Transportation Institute

The Virginia Tech Transportation Institute (VTTI) is collaborating with various stakeholders to study challenging, dynamic scenarios involving automated driving systems, such as encounters with public safety providers.

VTTI says the team will also address ways to facilitate communications between these systems and their supporting physical infrastructure.

The research is funded by a $7.5 million grant from the U.S. DOT—one of two competitive federal awards that the transportation institute received in September. Additional support is provided by the Virginia DOT and cost share from the project team members.

"Virginia is proud of VTTI as the recipient of this U.S. DOT grant,” Virginia Director of Transportation Research and Innovation Cathy McGhee said in a statement. “Our partnership in researching automated vehicles and their interaction with work zones and incident scenes is crucial in finding ways to reduce injuries and fatalities of those who work in and respond to events on our roadways.”

VTTI leaders say the dynamic nature of public safety scenarios could prove challenging for automated driving systems.

Over the next three years, the research team plans to identify solutions and build highly automated “Level 4” reference vehicles along with the connected infrastructure needed to support them. In the last phase of the project, they intend to hold three demonstrations to showcase the technology safely navigating challenging scenarios. The demos will take place in the greater Washington D.C. area on the I-95 Express Lanes operated by Transurban.

VTTI says that following the on-road demonstrations, the Global Center for Automotive Performance (GCAPS) will then process the vehicle data that was collected to create 3-D simulations of the driving scenarios. The simulations could help inform machine learning algorithms, industry and national standards, and educational materials for public service providers.

The on-road trials are anticipated to take place in 2022.


SOURCE: Virginia Tech Transportation Institute

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