ArticleScientific data2025
A naturalistic trajectory dataset with dense interaction for autonomous driving.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- A Fine-Grained Lightweight Urban Signalized-Intersection Dataset of Dense Conflict Trajectories.Scientific data · 2026Article
- CRFusion: a novel LiDAR-camera fusion network for BEV map construction.Scientific reports · 2026Article
- A naturalistic trajectory dataset with dense interaction for autonomous driving.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Driving interaction, a critical yet underrepresented element in trajectory datasets, is central to the development and evaluation of autonomous driving systems. This work presents InterHub, a curated dataset of dense multi-agent interaction events, derived from large-scale naturalistic driving recordings. We employ formal methods to describe and extract multi-agent interaction events, exposing the limitations of existing autonomous driving solutions. The dataset is accompanied by an open-source toolkit that enables users to expand InterHub by mining additional interaction events from both public and private driving data. By offering a unified taxonomy, rich annotations, and extensible tools, InterHub supports diverse research needs-from interaction behavior modeling to algorithm benchmarking-and promotes reproducibility, scalability, and cross-dataset comparison in autonomous driving studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.