Original title: Understanding Autonomous Driving Datasets by Describing Differences between Image Subsets in Natural Language
Julian Truetsch, Felix Hauser, Christoph Stiller, Frank Bieder · September 2026
Self-driving cars can fail if their real-world environment does not match their training data. Today, engineers mostly inspect driving data by hand or rely on simple metadataInformation like labels or tags attached to data, which often lacks semantic depth for understanding differences. tags, which is slow and misses fine details. To fix this, researchers explored set difference captioningA method that generates a natural language description of differences between two groups of images.. This technique compares two groups of images and automatically writes a plain-English description explaining how they differ. The team tailored this approach to autonomous driving by first running object detection. Instead of analyzing whole pictures, the system zooms in on cropped boxes around specific objects. This makes it easier to combine clues and link differences directly to particular things or types of objects. The researchers also created a new test dataset called AD-Diff Bench to test how well this method catches subtle, sparse differences in driving scenes.
Imagine you have two photo albums from different cities and want to know what changed without looking at every picture. One album might have more tall buildings, while the other has more parks, but spotting all the differences alone is slow and easy to miss. In this paper, the system does something similar: it compares two sets of driving images and automatically describes key differences by focusing on specific objects in each image, like which ones are missing or added.
This helps autonomous driving engineers spot gaps in their training data without manual checks, making cars safer by ensuring they handle diverse real-world conditions. It affects anyone relying on autonomous vehicles, as better dataset understanding reduces performance risks.
The abstract states this is a step towards practical introspection, so it may not cover all aspects of dataset analysis. It is restricted to autonomous driving and uses a specific benchmark, AD-Diff Bench, which might not generalize to other domains. Experiments are limited to open-weight models, and the focus is on object-centric differences, possibly missing broader scene changes.
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Understanding Autonomous Driving Datasets by Describing Differences between Image Subsets in Natural Language — Julian Truetsch, Felix Hauser, Christoph Stiller et al.
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.