Project: #78 Understanding and Guiding Pedestrian and Crowd Motion Progress Report - Reporting Period Ending: Sept. 30, 2020 Principal Investigator: Umit Ozguner Status: Overdue Project Start Date: Nov. 30, 2016 End Date: Jan. 31, 2020 Research Type: Advanced Grant Type: Research Grant Program: FAST Act - Mobility National (2016 - 2022) Grant Cycle: Mobility21 - The Ohio State University Progress Report (Last Updated: May 1, 2020, 10:10 a.m.) % Project Completed to Date: 100 % Grant Award Expended: 0 % Match Expended & Document: 0 USDOT Requirements Accomplishments A public database of slow-moving vehicles going through pedestrians or pedestrian crowds was established. Trajectories of both pedestrians and slow-moving vehicles were extracted as ground truth. The database has two parts, controlled scenarios of several types of fundamental vehicle-pedestrian interaction conducted at The Ohio State University, and natural campus scenarios recorded in Dalian University of Technology as part of collaboration. The database was published online as repositories. A simulation study of planning longitudinal motion of automated vehicles going through a crossing crowd was conducted the previous year to reduce the time the vehicle uses to go through such a scenario. This year, to aid in the above scenarios, vehicle effects on the pedestrians were investigated by developing effect modeld (somewhat similar to potential fields.) The corresponding results were submitted and presented at different conferences and workshops. The collected data was standardized and formatted together with existing crowd motion datasets. A benchmark was provided by refining, calibrating, and evaluating the crowd motion based on the database. Real-time crowd motion applications were explored to demonstrate the guidance of pedestrian crowds. Impacts The published database can benefit a variety of pedestrian related research in transportation systems, which includes modeling and analyzing interactive pedestrian motion, and improving and validating bird-view pedestrian detection and tracking. The incorporation of crowd motion into the planning of automated vehicles gave new ideas for the community of automated vehicles to deal with pedestrian-dense scenarios. Other Database links: - CITR dataset: https://github.com/dongfang-steven-yang/vci-dataset-citr - DUT dataset: https://github.com/dongfang-steven-yang/vci-dataset-dut Website mentioning Project: https://car.osu.edu/news/2020/03/pedestrian-trajectory-data-receives-international-attention Outcomes New Partners Professor Linhui Li, Dalian University of Technology, China A collaboration was established with a European project, SocialCars. One researcher from the prooject, Fatema Johora, from Clausthal University of Technology in Germany has joined the team at Ohio State to work on different aspects of project about modeling multi-pedestrian behavior in mixed traffic scenarios. Issues None