Dataset Card for ZOD

The Zenseact Open Dataset (ZOD) is a large multi-modal autonomous driving (AD) dataset created by researchers at Zenseact. It was collected over a 2-year period in 14 different European counties, using a fleet of vehicles equipped with a full sensor suite. The dataset consists of three subsets: Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatiotemporal learning, sensor fusion, localization, and mapping. Together with the data, we have developed a SDK containing tutorials, downloading functionality, and a dataset API for easy access to the data. The development kit is available on Github.

Dataset Details

Dataset Description

ZOD is a large-scale diverse, multimodal AD dataset, collected over two years in various European countries. It has highest-range and resoutions sensors and contains data from various traffic scenarios.

• Curated by: Zenseact AB
• Funded by: Zenseact AB
• Shared by: Zenseact AB
• Language(s): English
• License: CC BY-SA

Dataset Sources [optional]

• Repository: https://github.com/zenseact/zod
• Paper: https://arxiv.org/abs/2305.02008
• Website: https://zod.zenseact.com/

Uses

Direct Use

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Out-of-Scope Use

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Dataset Structure

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Dataset Creation

Curation Rationale

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Source Data

Data Collection and Processing

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Who are the source data producers

The Zenseact Open Dataset (ZOD) is the property of Zenseact AB (© 2022 Zenseact AB), and is collected by several developmental vehicles with an identical sensor layout.

Annotations [optional]

Annotation process

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Personal and Sensitive Information

To protect the privacy of every individual in our dataset, and to comply with privacy regulations such as the European Union’s General Data Protection Regulation (GDPR), we employ third-party services (Brighter AI) to anonymize all images in our dataset. The anonymization should protect all personally identifiable information in the images, including faces and license plates.

For Frames we supply two types of anonymization, namely Deep Neural Anonymization Technology (DNAT) and blurring. We studied the effects that these two anonymization methods have on downstream computer vision tasks and found no significant difference between the two. For more details about the experiments, see our paper. After this study, we anonymized the Sequences and Drives using the blurring anonymization method only.

Bias, Risks, and Limitations

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Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation [optional]

@inproceedings{alibeigi2023zenseact, title={Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving}, author={Alibeigi, Mina and Ljungbergh, William and Tonderski, Adam and Hess, Georg and Lilja, Adam and Lindstrom, Carl and Motorniuk, Daria and Fu, Junsheng and Widahl, Jenny and Petersson, Christoffer}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, year={2023} }

Glossary

ZOD stands for Zenseact Open Dataset. AD stands for Autonomous Driving.

Dataset Card Authors [optional]

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Dataset Card Contact

[email protected]