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

翻译：现有的自动驾驶数据集往往缺乏多样性和远距离感知能力，主要聚焦于360度感知与时序推理。为弥补这一不足，我们推出了Zenseact开放数据集（ZOD），这是一个在欧洲多国历经两年采集的大规模多样化多模态数据集，覆盖面积是现有数据集的9倍。ZOD相比同类数据集拥有最远探测距离与最高分辨率的传感器，同时配备了详细的2D与3D目标（最远达245米）关键帧标注、道路实例/语义分割、交通标志识别及道路分类标注。我们认为这种独特组合将促进远距离感知与多任务学习的突破性进展。该数据集由Frames、Sequences和Drives三部分组成，旨在兼顾数据多样性与时空学习、传感器融合、定位及建图支持。Frames包含10万张精选相机图像及两秒辅助传感器数据，而1473个Sequences和29个Drives分别包含20秒和数分钟的完整传感器套件数据。ZOD是唯一采用宽松许可协议发布的大规模自动驾驶数据集，既可用于研究也可用于商业用途。更多信息及完整开发工具包请访问https://zod.zenseact.com