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Life sciences · Preprint
arXiv · September 3, 2026
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This preprint describes a natural-language captioning method and introduces AD-Diff Bench, a new benchmark for characterizing differences between autonomous driving dataset subsets. The work aims to enable human-interpretable dataset analysis at scale, addressing domain shift risk, but provides no empirical validation of the method's effectiveness, safety impact, or comparison to existing approaches.
Preprint. Autonomous driving datasets; no human subjects or clinical populations.. Intervention: Set difference captioning method adapted to autonomous driving using object-centric patches; AD-Diff Bench benchmark..
Proposes set difference captioning adapted to autonomous driving using object-centric patches from object detection Introduces AD-Diff Bench as a new benchmark for in-domain evaluation Restricts experiments to open-weight models to support reproducibility and deployment
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
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This is a methods paper introducing a benchmark and technical approach for dataset analysis in autonomous driving, not a clinical or safety validation study, with no comparative efficacy data or real-world deployment evidence.
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Understanding the composition of large-scale autonomous driving datasets is essential for safety, robustness, and reliable operation across domains. For example, domain shift between locations could lead to the operating environment being misaligned with the training data, resulting in potentially dangerous performance degradation. Yet, existing data analysis pipelines largely rely on metadata, predefined labels, or manual inspection, which provide limited semantic insight or do not scale. This paper studies set difference captioning: given two subsets of images, the goal is to produce a natural-language hypothesis describing differences between the target and reference set. Building on a two-stage formulation, we adapt the method to autonomous driving by focusing on object-centric patches derived from object detection, which simplifies aggregation and enables attribution of differences to specific object instances or categories. To evaluate this setting in-domain, we introduce a new benchmark, AD-Diff Bench. Low-concentration experiments assess the suitability of set-difference-captioning approaches to sparse, real-world differences. We restrict our experiments to open-weight models to support reproducibility and ease of deployment. The proposed benchmark and analysis provide a step towards practical, human-interpretable dataset introspection for autonomous driving datasets. Our implementation and benchmark dataset are available at https://github.com/KIT-MRT/AD-Diff
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