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. The dataset is accompanied by an extensive development kit. Data and more information are available online (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，而是聚焦于360度感知和时间推理。为填补这一空白，我们提出了Zenseact开放数据集（ZOD），这是一个跨两年在多个欧洲国家收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配备了同类数据集中最高射程和最高分辨率的传感器，并包含详细的2D和3D物体（最远达245米）关键帧标注、道路实例/语义分割、交通标志识别以及道路分类。我们相信，这一独特组合将推动远距离感知和多任务学习领域的突破。该数据集由帧（Frames）、序列（Sequences）和行程（Drives）三部分组成，旨在兼顾数据多样性并支持时空学习、传感器融合、定位与建图。帧包含10万张精选相机图像及两秒的辅助传感器数据；1473个序列和29个行程则分别包含20秒和数分钟的全套传感器数据。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，允许研究及商业用途。该数据集附带丰富的开发工具包，数据及更多信息可通过在线渠道获取。