Ad-datasets: a meta-collection of data sets for autonomous driving

Abstract

Autonomous driving is among the largest domains in which deep learning has been fundamental for progress within the last years. The rise of datasets went hand in hand with this development. All the more striking is the fact that researchers do not have a tool available that provides a quick, comprehensive and up-to-date overview of data sets and their features in the domain of autonomous driving. In this paper, we present ad-datasets, an online tool that provides such an overview for more than 150 data sets. The tool enables users to sort and filter the data sets according to currently 16 different categories. ad-datasets is an open-source project with community contributions. It is in constant development, ensuring that the content stays up-to-date.

arXiv commentDaniel Bogdoll and Felix Schreyer contributed equally. Accepted for publication at VEHITS 2022

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Cited by in corpus (11)

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• Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets
• Quantification of Actual Road User Behavior on the Basis of Given Traffic Rules
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• Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving
• Conditioning Latent-Space Clusters for Real-World Anomaly Classification
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