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Zenseact Open Dataset : A large-scale and diverse multimodal dataset for autonomous driving

Alibeigi, Mina ; Ljungbergh, William ; Tonderski, Adam LU orcid ; Hess, Georg ; Lilja, Adam ; Lindström, Carl ; Motorniuk, Daria ; Fu, Junsheng ; Widahl, Jenny and Petersson, Christoffer (2023) 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023 In Proceedings of the IEEE International Conference on Computer Vision p.20121-20131
Abstract

Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360° perception and temporal reasoning. To address this gap, we introduce Zenseact Open Dataset (ZOD), a large- scale and diverse multimodal dataset collected over two years in various European countries, covering an area 9×that of existing datasets. ZOD boasts the highest range and resolution sensors among comparable datasets, coupled with detailed keyframe annotations for 2D and 3D objects (up to 245m), road instance/semantic segmentation, traffic sign recognition, and road classification. We believe that this unique combination will facilitate breakthroughs in long-range perception and multi-task learning. The dataset... (More)

Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360° perception and temporal reasoning. To address this gap, we introduce Zenseact Open Dataset (ZOD), a large- scale and diverse multimodal dataset collected over two years in various European countries, covering an area 9×that of existing datasets. ZOD boasts the highest range and resolution sensors among comparable datasets, coupled with detailed keyframe annotations for 2D and 3D objects (up to 245m), road instance/semantic segmentation, traffic sign recognition, and road classification. We believe that this unique combination will facilitate breakthroughs in long-range perception and multi-task learning. The dataset is composed of Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatio-temporal learning, sensor fusion, localization, and mapping. Frames consist of 100k curated camera images with two seconds of other supporting sensor data, while the 1473 Sequences and 29 Drives include the entire sensor suite for 20 seconds and a few minutes, respectively. ZOD is the only large-scale AD dataset released under a permissive license, allowing for both research and commercial use. More information, and an extensive devkit, can be found at zod.zenseact.com.

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Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
series title
Proceedings of the IEEE International Conference on Computer Vision
pages
11 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
conference location
Paris, France
conference dates
2023-10-02 - 2023-10-06
external identifiers
  • scopus:85180348107
ISSN
1550-5499
ISBN
9798350307184
DOI
10.1109/ICCV51070.2023.01846
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2023 IEEE.
id
502752a1-ea1d-4dad-9c98-5425da2f534a
date added to LUP
2025-01-31 10:20:50
date last changed
2025-04-04 13:59:55
@inproceedings{502752a1-ea1d-4dad-9c98-5425da2f534a,
  abstract     = {{<p>Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360° perception and temporal reasoning. To address this gap, we introduce Zenseact Open Dataset (ZOD), a large- scale and diverse multimodal dataset collected over two years in various European countries, covering an area 9×that of existing datasets. ZOD boasts the highest range and resolution sensors among comparable datasets, coupled with detailed keyframe annotations for 2D and 3D objects (up to 245m), road instance/semantic segmentation, traffic sign recognition, and road classification. We believe that this unique combination will facilitate breakthroughs in long-range perception and multi-task learning. The dataset is composed of Frames, Sequences, and Drives, designed to encompass both data diversity and support for spatio-temporal learning, sensor fusion, localization, and mapping. Frames consist of 100k curated camera images with two seconds of other supporting sensor data, while the 1473 Sequences and 29 Drives include the entire sensor suite for 20 seconds and a few minutes, respectively. ZOD is the only large-scale AD dataset released under a permissive license, allowing for both research and commercial use. More information, and an extensive devkit, can be found at zod.zenseact.com.</p>}},
  author       = {{Alibeigi, Mina and Ljungbergh, William and Tonderski, Adam and Hess, Georg and Lilja, Adam and Lindström, Carl and Motorniuk, Daria and Fu, Junsheng and Widahl, Jenny and Petersson, Christoffer}},
  booktitle    = {{Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023}},
  isbn         = {{9798350307184}},
  issn         = {{1550-5499}},
  language     = {{eng}},
  pages        = {{20121--20131}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{Proceedings of the IEEE International Conference on Computer Vision}},
  title        = {{Zenseact Open Dataset : A large-scale and diverse multimodal dataset for autonomous driving}},
  url          = {{http://dx.doi.org/10.1109/ICCV51070.2023.01846}},
  doi          = {{10.1109/ICCV51070.2023.01846}},
  year         = {{2023}},
}