Third Generation Simulation (TGSIM) data

Third Generation Simulation (TGSIM) data: a closer look at the impacts of automated driving systems on human behavior (Prof. Hamdar)

Automated Driving Systems (ADS) has the potential to significantly improve congestion, emissions, energy consumption, and safety throughout the roadway system. A key factor in realizing this potential is characterize the impacts of ADS on human decision-making (including driver, pedestrian, bicyclists, and scooterist behaviors). Unfortunately, data from human-ADS interactions are considerably limited and existing datasets are either based on controlled environment testing or only capture the interactions under very limited scenarios. Accordingly, in this project funded by the US Department of Transportation/Federal Highway Administration, the research team from the George Washington University, Northwestern University, and the University of Illinois at Urbana Champaign, is collecting datasets from human-ADS interactions under a diverse set of scenarios in both highway and arterial environments. With special focus on urban multi-modal roadway networks, the data collected by the GW Transportation program is the first of its kind in naturalistic traffic conditions and will serve as a tool to create smart cities that leverages Connected and Automated Vehicles (CAVs) for safer and more efficient surface transportation.