首个真实自动驾驶模式采集的端到端数据集，用于评估量产车安全行为。
PAVE: An End-to-End Dataset for Production Autonomous Vehicle Evaluation
- 全由自动驾驶车辆在真实世界采集，覆盖多款市售车型
- 含32,727个关键帧，定位精度达0.8厘米，提供6秒前5秒后轨迹
- 支持驾驶意图、天气、交通密度等场景属性分析，适合安全评估研究
现有自动驾驶数据集（如KITTI、nuScenes、Waymo感知数据集）多通过人工驾驶或未知驾驶模式采集，仅适用于早期感知与预测训练。为评估黑箱控制下自动驾驶车辆的真实行为安全性，我们提出首个完全由自动驾驶模式在真实世界采集的端到端基准数据集。该数据集包含超过100小时自然驾驶数据，来自市场多款量产自动驾驶车辆。原始数据被分割为32,727个关键帧，每帧包含四路同步摄像头图像及高精度GNSS/IMU数据（定位精度0.8厘米）。每个关键帧提供过去6秒至未来5秒的20 Hz车辆轨迹，并附有周围车辆、行人、交通灯、交通标志的详细2D标注。关键帧具备丰富的场景级属性：驾驶员意图、区域类型（高速、城市道路、住宅区）、光照（白天、夜晚、黄昏）、天气（晴朗或雨天）、路面类型（铺装或未铺装）、交通密度与弱势道路使用者（VRU）密度、交通灯与交通标志类型（警告、禁止、指示）。为评估车辆安全性，采用端到端运动规划模型，在自动驾驶帧上实现1.4米平均位移误差（ADE）。数据集每周新增超10小时数据，持续扩展，为自动驾驶行为分析与安全评估提供可持续基础。PAVE数据集公开获取：https://hkustgz-my.sharepoint.com/:f:/g/personal/kema_hkust-gz_edu_cn/IgDXyoHKfdGnSZ3JbbidjduMAXxs-Z3NXzm005A_Ix9tr0Q?e=9HReCu。
原文摘要 · Abstract (English)
Most existing autonomous-driving datasets (e.g., KITTI, nuScenes, and the Waymo Perception Dataset), collected by human-driving mode or unidentified driving mode, can only serve as early training for the perception and prediction of autonomous vehicles (AVs). To evaluate the real behavioral safety of AVs controlled in the black box, we present the first end-to-end benchmark dataset collected entirely by autonomous-driving mode in the real world. This dataset contains over 100 hours of naturalistic data from multiple production autonomous-driving vehicle models in the market. We segment the original data into 32,727 key frames, each consisting of four synchronized camera images and high-precision GNSS/IMU data (0.8 cm localization accuracy). For each key frame, 20 Hz vehicle trajectories spanning the past 6 s and future 5 s are provided, along with detailed 2D annotations of surrounding vehicles, pedestrians, traffic lights, and traffic signs. These key frames have rich scenario-level attributes, including driver intent, area type (covering highways, urban roads, and residential areas), lighting (day, night, or dusk), weather (clear or rain), road surface (paved or unpaved), traffic and vulnerable road users (VRU) density, traffic lights, and traffic signs (warning, prohibition, and indication). To evaluate the safety of AVs, we employ an end-to-end motion planning model that predicts vehicle trajectories with an Average Displacement Error (ADE) of 1.4 m on autonomous-driving frames. The dataset continues to expand by over 10 hours of new data weekly, thereby providing a sustainable foundation for research on AV driving behavior analysis and safety evaluation. The PAVE dataset is publicly available at https://hkustgz-my.sharepoint.com/:f:/g/personal/kema_hkust-gz_edu_cn/IgDXyoHKfdGnSZ3JbbidjduMAXxs-Z3NXzm005A_Ix9tr0Q?e=9HReCu.
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