United States Department of Transportation
i
Driving Behavioral Learning Leveraging Sensing Information from Innovation Hub [Supporting Dataset]
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2022-09-01
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Alternative Title:Data for "CAIT-UTC-REG46: Driving behavioral learning leveraging sensing information from Innovation Hub"
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Edition:Final report, 1/1/2021 – 6/30/2022
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Abstract:With the accelerated deployment of connected and automated vehicle (CAV) technologies, public agencies have urgent needs on how to utilize these rich data sources of CAVs to improve traffic mobility, safety, and environmental and energy impact. This research will tackle one of the big data challenges, which is mining driving behavior patterns using vehicle data sources. We leverage physics-informed deep learning and uncertainty quantification methods to predict drivers’ car-following behavior using historical trajectories. A digital twin is developed leveraging the COSMOS testbed deployed near Columbia campus to validate the model algorithms and results. Moreover, an app is developed that captures drivers' faces. On the AWS server, face detection algorithms are applied to analyze drivers' moods and attention. Combined with the vehicle information (e.g., speed, acceleration) that is detected from roadside cameras, a model is established to predict the safety index of the driver and the roadway. The project outcome will be valuable for digital sibling simulation development and applications and future deployment of AVs that need to drive alongside humans.
The total size of the described file is 1.2 MB. The .csv, Comma Separated Value, file is a simple format that is designed for a database table and supported by many applications. The .csv file is often used for moving tabular data between two different computer programs, due to its open format.
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Content Notes:National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. The current level of dataset documentation is the responsibility of the dataset creator. NTL staff last accessed this dataset at its repository URL on 2023-07-27. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
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