The DOI displayed 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: 10.4121/9caa1e6c-9abd-4e36-ae28-c9ea4542d940

Jiao, Yiru; Simeon Calvert; van Cranenburgh, Sander; van Lint, Hans (2025): Data underlying the publication: Learning collision risk proactively from naturalistic driving data at scale. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/9caa1e6c-9abd-4e36-ae28-c9ea4542d940.v1

Categories

• Civil Engineering
• Other Information and Computing Sciences
• Ground Transport
• Engineering
• Transport
• Information and Computing Sciences

Keywords

collision risk naturalistic driving data risk quantification Road safety trajectory reconstruction

Licence

CC BY 4.0

Interoperability

• RO-Crate Metadata

Collection

• Data underlaying the PhD Dissertation of Yiru Jiao

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by Yiru Jiao , Simeon Calvert , Sander van Cranenburgh , Hans van Lint

This dataset includes the resulting data of the research: Learning collision risk proactively from naturalistic driving data at scale. It is organised into two zipped files: one from the PreparedData folder and another from the ResultData folder. The PreparedData archive contains the processed and segmented training samples from highD, ArgoverseHV, and SHRP2 NDS, along with the checkpoints and loss logs generated during GSSM posterior inference. The ResultData archive gathers the outcomes of the full experimental pipeline, including test set preparation, first-stage safety evaluations, and second-stage conflict and risk analyses. Overall, this dataset supports the research aimed at learning collision risk from naturalistic driving interactions, where a context-aware, scalable, and generalisable method is proposed. The scripts that produced these data are open-sourced at https://github.com/Yiru-Jiao/GSSM

History

• 2025-06-13 first online, published, posted

Publisher

4TU.ResearchData

Format

HDF5, CSV, PTH

Associated peer-reviewed publication

Learning Collision Risk from Naturalistic Driving with Generalised Surrogate Safety Measures

Derived from

• https://doi.org/10.15787/VTT1/DEDACT

Funding

• TU Delft AI Labs programme [more info...] Delft University of Technology

Organizations

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning, Traffic Systems Engineering