自動駕駛的未來之路：KITScenes多模態數據集

摘要

現有的自動駕駛資料集已促成重大進展，但在感測器保真度、地圖完整性及地理多樣性方面仍有不足。我們提出KITScenes Multimodal，這是一個以高保真感測器與地圖為核心建構的歐洲資料集。全同步化的感測器套件結合高解析全域快門攝影機、超過400公尺的長距離光達、4D成像雷達，以及冗餘GNSS/INS定位系統。據我們所知，我們的高清地圖是所有感測器資料集中最完整的，並且已透過基於開源軟體的自動駕駛試驗驗證。本資料集首次在公開資料中，將所有與駕駛相關的交通元素（如交通號誌）以三維方式繪製至重投影精確等級，並具備完整的拓撲連通性。我們在城市中記錄資料，這些城市擁有非正交街道布局與混合交通模式，從而透過擴展可取得的地理多樣性，補充現有資料集之不足。我們同時引入四個基準測試，每一項均推動具身AI的空間學習進展：線上高清地圖建構、長距離深度估計、新視角合成，以及端到端駕駛。專案頁面：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/