Abstract
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: this https URL
Abstract (translated)
自动驾驶系统的有效性在很大程度上取决于强大的预测和规划能力。然而，当前的基准测试受到缺乏密集交通场景数据的限制，而这些场景对于理解并建模道路使用者之间的复杂交互至关重要。为解决这一问题，我们与我们的工业合作伙伴DeepScenario合作，开发了DeepUrban——一个新的无人机数据集，旨在增强在密集城市环境下的轨迹预测和规划基准测试。DeepUrban提供了一套丰富的3D交通对象集合，这些对象是从高空约100米处拍摄的城市交叉口高分辨率图像中提取出来的。该数据集还通过详细的地图和场景信息得到了进一步的丰富，以支持高级建模和仿真任务。 我们评估了最先进的预测和规划方法，并进行了泛化能力实验。研究结果表明，在nuScenes基础上加入DeepUrban可以显著提高车辆预测和规划的准确性，ADE/FDE指标分别提高了44.1% / 44.3%。网站：[此处插入实际链接]
URL
https://arxiv.org/abs/2601.10554
