What's Happening

Photo: Graduate student, Dongfang Yang says the new dataset can benefit the design of more realistic simulation models for vehicle-pedestrian interaction and can also be used to improve the accuracy of pedestrian behavior prediction in autonomous driving.

March 30, 2020

“There are constantly new studies and reports coming out about autonomous and connected vehicles and how they interact with other vehicles, traffic lights, and even the roads they are driving on via underground cables, but it is equally important to understand how these vehicles interact with the pedestrians they come in contact with.  Sponsored by the U.S. Department of Transportation, Carnegie Mellon Mobility21 National University Transportation Center, a team of faculty and students led by Emeritus Professor, Umit Ozguner at Ohio State’s Center for Automotive Research have developed a new dataset that can help researchers better understand vehicle-pedestrian interaction in crowded areas.”  Read more here.