  Existing datasets for autonomous driving (AD) often lack diversity and
long-range capabilities, focusing instead on 360{\deg} 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 9x 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 https://zod.zenseact.com
