Advanced Driver Assistance System-Equipped Vehicle Datasets Collected in Central Ohio: Final Report
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2024-03-01
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Edition:Final Report
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Abstract:Connected and automated vehicle (CAV) technology has been driving multi-billion-dollar investments for over a decade now. While no agreement exists on the exact timeframe and capabilities of these systems, the transportation industry agrees that CAV adoption will eventually occur. Infrastructure owners and operators (IOOs) are making decisions now that will shape the transportation system of the future and need high-resolution, naturalistic datasets for improved traffic simulation that include CAV operation on roadways. While original equipment manufacturers (OEMs) and vehicle-technology companies do have high volumes of high-resolution datasets, not all the data are publicly available to characterize the behavior of CAVs and their impact on transportation system performance. Ultimately, the lack of well-calibrated traffic simulation tools is preventing IOOs from making necessary infrastructure investment decisions that account for the presence of CAVs and other emerging technologies. This project developed a methodology to collect and process the raw vehicle sensor data to extract trajectory data about the instrumented subject vehicle (SV) and all adjacent vehicles (AdjVs) in the traffic stream that were perceived by the sensors. These publicly available datasets will enable researchers to develop models that characterize human-ADAS interactions under a diverse set of naturalistic traffic scenarios in highway and arterial environments. Using these data, researchers will be able to develop appropriate models to assess the impact of ADAS technologies and update current driver behavioral models using data collected during this project, including trajectories of SVs and AdjVs.
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