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Autonomous Delivery Vehicle as a Disruptive Technology: How To Shape the Future With a Focus on Safety? [Supporting Dataset]
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2022-09-10
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Edition:Final Report
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Abstract:The National Highway Traffic Safety Administration recently granted permission to deploy low-speed autonomous delivery vehicles (ADVs) on roadways. Although the mobility of ADVs is limited to low-speed roads and these vehicles are occupant less, frequent stops and mobility among residential neighborhoods cause safety-related concerns. There is consequently a need for a comprehensive safety impact analysis of ADVs. This study examined the safety implications and safety impacts of ADVs by using novel approaches. This research prepared several datasets such as fatal crash data, aggregated ADV trips and trajectories, and real-world crash data from the scenario design for an ADV-related operational design domain. Association rules mining was applied to the datasets to identify significant patterns. This study generated a total of 80 association rules that provide risk patterns associated with ADVs. The rules can be used as prospective benchmarks to examine how rule-based risk patterns can be reduced by ADVs that replace human-driven trips.
The total size of the described file is 2.7 MB. Files with the .xlsx extension are Microsoft Excel spreadsheet files. These can be opened in Excel or open-source spreadsheet programs. PDFs are used to display text and images and can be opened with any PDF reader or editor. 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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