自动驾驶的前路：KITScenes多模态数据集

摘要

现有自动驾驶数据集虽取得重大进展，但在传感器保真度、地图完整性和地理多样性方面仍存在不足。我们提出KITScenes Multimodal——一个基于高保真传感器与地图构建的欧洲数据集。该数据集采用全同步传感器套件，集成高分辨率全局快门相机、探测距离超400米的远距激光雷达、4D成像雷达及冗余GNSS/INS定位系统。据我们所知，其高清地图是现有传感器数据集中最完整的，已通过基于开源软件的自动驾驶测试验证。本数据集首次在公开数据集中，将所有驾驶相关交通要素（如交通信号灯）以三维形式精确映射至重投影精度级别，并具备完整拓扑连通性。数据采集于街道布局不规则、交通模式混合的城市，通过拓展地理多样性对现有数据集形成补充。我们还引入四项基准测试，旨在推进具身智能的空间学习：在线高清地图构建、远距离深度估计、新视角合成及端到端驾驶。项目页面：https://kitscenes.com/

English

Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. We present KITScenes Multimodal, a European dataset built around high-fidelity sensors and maps. Our fully synchronized sensor suite combines high-resolution global-shutter cameras, long-range lidar beyond 400m, 4D imaging radar, and redundant GNSS/INS localization. Our HD maps are, to our knowledge, the most complete of any sensor dataset, validated through autonomous driving trials on open-source software. For the first time in a public dataset, all driving-relevant traffic elements, such as traffic lights, are mapped in 3D to a reprojection-accurate level with full topological connectivity. Recorded in cities with irregular street layouts and mixed traffic modes, our dataset complements existing datasets by broadening the available geographic diversity. We also introduce four benchmarks, each advancing spatial learning for embodied AI: online HD map construction, long-range depth estimation, novel view synthesis, and end-to-end driving. Project page: https://kitscenes.com/