A Comparative Conflict Resolution Dataset Derived from Argoverse-2: Scenarios with vs. without Autonomous Vehicles

doi: 10.4121/8d6ee0b0-8ed5-43f3-b1c9-7665cc163e87.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/8d6ee0b0-8ed5-43f3-b1c9-7665cc163e87
Datacite citation style:
Li, Guopeng; Jiao, Yiru; Simeon Calvert; van Lint, Hans (2023): A Comparative Conflict Resolution Dataset Derived from Argoverse-2: Scenarios with vs. without Autonomous Vehicles. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/8d6ee0b0-8ed5-43f3-b1c9-7665cc163e87.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
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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.

  • 2023-09-13 first online, published, posted
one .zip file containing all the data, 1 python script, and 2 .csv meta datafile, and a readme.md file
  • MiRRORs (grant code 16270) NWO/TTW
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Transport & Planning


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