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

翻译：自动驾驶系统的效能关键取决于其鲁棒的预测与规划能力。然而，当前基准测试面临一个显著缺陷：缺乏密集交通场景，而这对于理解和建模道路使用者之间的复杂交互至关重要。为填补这一空白，我们与工业合作伙伴DeepScenario合作开发了DeepUrban——一个旨在增强针对密集城市环境的轨迹预测与规划基准的新型无人机数据集。DeepUrban提供了丰富的三维交通物体集合，这些数据提取自约100米高空拍摄的城市交叉路口高分辨率图像。该数据集进一步补充了全面的地图与场景信息，以支持高级建模与仿真任务。我们评估了最先进的预测与规划方法，并对其泛化能力进行了实验。研究结果表明，将DeepUrban加入nuScenes数据集可显著提升车辆预测与规划的准确性，在ADE/FDE指标上分别实现了高达44.1%/44.3%的改进。项目网站：https://iv.ee.hm.edu/deepurban