{"data":{"id":"10.4121/8d6ee0b0-8ed5-43f3-b1c9-7665cc163e87","type":"dois","attributes":{"doi":"10.4121/8d6ee0b0-8ed5-43f3-b1c9-7665cc163e87","prefix":"10.4121","suffix":"8d6ee0b0-8ed5-43f3-b1c9-7665cc163e87","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Li, Guopeng","givenName":"Guopeng","familyName":"Li","affiliation":[],"nameIdentifiers":[]},{"name":"Jiao, Yiru","givenName":"Yiru","familyName":"Jiao","affiliation":[],"nameIdentifiers":[]},{"name":"Calvert, Simeon","nameType":"Personal","givenName":"Simeon","familyName":"Calvert","affiliation":[],"nameIdentifiers":[{"nameIdentifier":"0000-0002-1173-0071","nameIdentifierScheme":"https://orcid.org/"}]},{"name":"van Lint, Hans","nameType":"Personal","givenName":"Hans","familyName":"van Lint","nameIdentifiers":[{"nameIdentifier":"0000-0003-1493-6750","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]}],"titles":[{"title":"A Comparative Conflict Resolution Dataset Derived from Argoverse-2: Scenarios with vs. without Autonomous Vehicles"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2024,"subjects":[{"subject":"Civil Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0905"},{"subject":"FOS: Civil engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Mechanical Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0913"},{"subject":"FOS: Mechanical engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Interdisciplinary Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0915"},{"subject":"FOS: Other engineering and technologies","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Ground Transport","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"8801"},{"subject":"Automotive Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0902"},{"subject":"Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"09"},{"subject":"Transport","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"88"},{"subject":"Autonomous vehicles"},{"subject":"Trajectory dataset"},{"subject":"Conflict resolution"},{"subject":"traffic safety"},{"subject":"Time: 2019-2023"}],"contributors":[{"name":"TU Delft, Faculty Of Civil Engineering And Geosciences, Department Of Transport \u0026 Planning","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2024-08-14","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.48550/arxiv.2308.13839","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":["one .zip file containing all the data, 1 python script, and 2 .csv meta datafile, and a readme.md file"],"version":"2","rightsList":[{"rights":"Creative Commons Attribution Non Commercial Share Alike 4.0 International","rightsUri":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-nc-sa-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"As the deployment of autonomous vehicles (AVs) becomes increasingly prevalent, ensuring safe and smooth interactions between AVs and other human agents is of critical importance. In the urban environment, how vehicles resolve conflicts has significant impacts on both driving safety and traffic efficiency. To expedite the studies on evaluating conflict resolution in AV-involved and AV-free scenarios at unsignalized intersections, this paper presents a high-quality dataset derived from the open Argoverse-2 motion forecasting data. First, scenarios of interest are selected by applying a set of heuristic rules regarding post-encroachment time (PET), minimum distance, trajectory crossing, and speed variation. Next, the quality of the raw data is carefully examined. We found that position and speed data are not consistent in Argoverse-2 data and its improper processing induced unnecessary errors. To address these specific problems, we propose and apply a data processing pipeline to correct and enhance the raw data. As a result, 5k+ AV-involved scenarios and 16k+ AV-free scenarios with smooth and consistent position, speed, acceleration, and heading direction data are obtained. Further assessments show that this dataset comprises diverse and balanced conflict resolution regimes. This informative dataset provides a valuable resource for researchers and practitioners in the field of autonomous vehicle assessment and regulation.","descriptionType":"Abstract"}],"geoLocations":[{"geoLocationPlace":"Austin, Detroit, Miami, Pittsburgh, Palo Alto, and Washington, 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