Zero-Label Driving Scenario Complexity Detection via Joint Embedding Predictive Architecture
A novel method detects driving scenario complexity without any labels by using a Joint Embedding Predictive Architecture (JEPA) trained on structured agent state data from the nuPlan mini dataset. Temporal prediction error serves as a zero-shot complexity score, effectively distinguishing complex scenarios (e.g., unprotected turns, crosswalk interactions) from simple ones (e.g., lane-following). Downstream anomaly detection achieves Average Precision of 0.512, surpassing a 0.436 chance baseline.
[2606.28383] Zero-Label Driving Scenario Complexity Detection via Joint Embedding Predictive Architecture
[Submitted on 21 Jun 2026]
Title:Zero-Label Driving Scenario Complexity Detection via Joint Embedding Predictive Architecture
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Abstract:Identifying complex and safety-critical driving scenarios in large unlabelled datasets is an important but expensive problem. Existing approaches rely on human annotators, supervised classifiers, or carefully engineered rule sets, all of which require substantial prior knowledge about what constitutes a difficult scenario. We ask whether a model can discover scenario complexity on its own, with no labels at any stage. We train a minimal Joint Embedding Predictive Architecture (JEPA) on structured agent state data from the nuPlan mini dataset and use the temporal prediction error as a zero-shot complexity score. Without access to any ground-truth labels during training or evaluation setup, the model assigns significantly higher scores to scenarios involving unprotected turns, crosswalk interactions, and pedestrian proximity, and significantly lower scores to lane-following and stationary-traffic scenarios. We validate this finding through four ablation experiments that isolate the source of the signal, and through a downstream anomaly detection evaluation that achieves Average Precision of 0.512 against a 0.436 chance baseline. The results show that temporal prediction error in a self-supervised latent world model is a practical proxy for driving scenario complexity.
Comments: 12 pages, 6 figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2606.28383 [cs.CV]
(or arXiv:2606.28383v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.28383
arXiv-issued DOI via DataCite
Submission history
From: Santosh Jaiswal [view email] [v1] Sun, 21 Jun 2026 05:26:58 UTC (877 KB)
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