United States Department of Transportation
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Improving Methods to Measure Attentiveness through Driver Monitoring [Supporting Dataset]
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2022-06-24
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Edition:Final research
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Abstract:Driver inattention poses a significant problem on today’s roadways, increasing risk for all road users. This report details our efforts in developing algorithms to detect driver inattention. A benchmark dataset was developed based on video review of driving events. Buffer-based algorithms were developed and compared using this benchmark dataset. The benchmark events were also used as a training dataset for machine learning models. Driver glance locations were important for determining driver attentiveness. In addition, vehicle speed was important for understanding the driving context, which was found to have a large impact on driver behavior. The total size of the described file is 35.8 MB. Files with the .xlsx extension are Microsoft Excel spreadsheet files. These can be opened in Excel or open-source spreadsheet programs. A TAB file is a tabular data file that can be opened using open source programs such as Notepad or other open source, tabular file readers.
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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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