用无人机影像构建城市密集交通数据集，提升自动驾驶轨迹预测与规划性能。
DeepUrban: Interaction-Aware Trajectory Prediction and Planning for Automated Driving by Aerial Imagery
- 基于高空无人机图像提取3D交通目标，构建高密度城市数据集
- 在nuScenes上加入DeepUrban后，车辆预测与规划准确率提升44.1%/44.3%
- 适合研究复杂交互场景的自动驾驶轨迹预测与规划方向
自动驾驶系统的有效性高度依赖于可靠的轨迹预测与路径规划能力。然而，现有基准测试因缺乏高密度交通场景而受限，难以充分建模道路使用者间的复杂交互。为此，我们与工业合作伙伴DeepScenario合作，推出DeepUrban——一个面向密集城市环境的新一代无人机数据集，旨在增强轨迹预测与规划基准。该数据集通过约100米高空拍摄的高分辨率图像，提取丰富的3D交通物体，并整合全面的地图与场景信息，支持高级建模与仿真任务。我们在SOTA预测与规划方法上进行评估，并测试了泛化能力。结果表明，将DeepUrban加入nuScenes后，车辆预测与规划的ADE/FDE指标分别提升最高达44.1%/44.3%。
原文摘要 · Abstract (English)
The efficacy of autonomous driving systems hinges critically on robust prediction and planning capabilities. However, current benchmarks are impeded by a notable scarcity of scenarios featuring dense traffic, which is essential for understanding and modeling complex interactions among road users. To address this gap, we collaborated with our industrial partner, DeepScenario, to develop DeepUrban-a new drone dataset designed to enhance trajectory prediction and planning benchmarks focusing on dense urban settings. DeepUrban provides a rich collection of 3D traffic objects, extracted from high-resolution images captured over urban intersections at approximately 100 meters altitude. The dataset is further enriched with comprehensive map and scene information to support advanced modeling and simulation tasks. We evaluate state-of-the-art (SOTA) prediction and planning methods, and conducted experiments on generalization capabilities. Our findings demonstrate that adding DeepUrban to nuScenes can boost the accuracy of vehicle predictions and planning, achieving improvements up to 44.1 % / 44.3% on the ADE / FDE metrics. Website: https://iv.ee.hm.edu/deepurban
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