Project: #78 Understanding and Guiding Pedestrian and Crowd Motion Progress Report - Reporting Period Ending: Sept. 30, 2018 Principal Investigator: Umit Ozguner Status: Active Start Date: Nov. 30, 2016 End Date: Sept. 30, 2019 Research Type: Advanced Grant Type: Research Grant Program: FAST Act - Mobility National (2016 - 2022) Grant Cycle: 2017 Mobility21 UTC Progress Report (Last Updated: Oct. 3, 2018, 10:07 a.m.) % Project Completed to Date: 60 % Grant Award Expended: 0 % Match Expended & Document: 0 USDOT Requirements Accomplishments We constructed a framework that combines pedestrian detection via multiple sensors, vehicle-crowd interactive scenario prediction, and investigated approaches to improve the driving efficiency of autonomous vehicles within crowds. Methods of pedestrian detection on different types of sensors were introduced. Data was collected on the Campus of the Ohio State University. The data is being used to ascertain the parameters of our pedestrian/crowd simulation models. The corresponding initial results were presented at different Conferences. A simulation case study was conducted to demonstrate one of the proposed approaches for improving driving efficiency. Impacts The proposed framework has the potential to solve transportation problems in shared spaces where crowds of pedestrians and autonomous vehicles interact with each other. It will affect the control and guidance approaches taken for slow-moving platforms (scooters, personal mobility devices, motorized wheelchairs) of the future. Other We are preparing a data base of video images of a small wheeled vehicle approaching and passing through a crowd of pedestrians. Outcomes New Partners Professor Kazuya Takeda, Nagoya University, Japan. Issues None