%0 Generic
%A Jiao, Yiru
%A Calvert, Simeon
%A van Cranenburgh, Sander
%A van Lint, Hans
%D 2025
%T Data underlying the publication: Learning collision risk proactively from naturalistic driving data at scale
%U 
%R 10.4121/9caa1e6c-9abd-4e36-ae28-c9ea4542d940.v1
%K Road safety
%K collision risk
%K risk quantification
%K trajectory reconstruction
%K naturalistic driving data
%X <p>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</p>
%I 4TU.ResearchData