Asleep at the Wheel: JEPA's Limitations in Evaluating Novel Driving Data

Modern autonomous-driving fleets record far more video than human reviewers can inspect. This motivates the need for an automatic clip triage mechanism, to surface rare and review-worthy clips, so that driving models can be fine-tuned to better handle unideal circumstances. We test a label-free approach that scores clips by the prediction-error "novelty" of a self-supervised joint-embedding predictive architecture (JEPA); a frozen V-JEPA video encoder is paired with a lightweight predictor head

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ethics.ai (2 August 2026), “Asleep at the Wheel: JEPA's Limitations in Evaluating Novel Driving Data,” evidence record 16131, https://ethics.ai/record/16131 (originally published by arXiv fairness query).

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