论文标题
ITHACA365：在重复且充满挑战的天气条件下数据集和驾驶感知
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions
论文作者
论文摘要
由于大规模数据集的可用性，通常在特定位置和良好的天气条件下收集的大规模数据集，近年来，自动驾驶汽车的感知进步已加速。但是，为了达到高安全性，这些感知系统必须在多种天气条件下进行稳健运行，包括雪和雨。在本文中，我们提出了一个新数据集，可以通过新的数据收集过程启用强大的自动驾驶 - 在不同的场景（城市，高速公路，农村，校园），天气（雪，雨，太阳），时间（日/夜），时间（日夜）和交通状况（行人，骑自行车的人，骑自行车的人和汽车）的情况下，沿着15公里的路线反复记录数据。该数据集包括来自摄像机和激光雷达传感器的图像和点云，以及高精度GPS/ins以在跨路线上建立对应关系。该数据集包括使用Amodal面具的道路和对象注释，以捕获部分遮挡和3D边界框。我们通过分析基准在道路和对象，深度估计和3D对象检测中的性能来证明该数据集的独特性。重复的路线为对象发现，持续学习和异常检测打开了新的研究方向。链接到ITHACA365：https：//ithaca365.mae.cornell.edu/
Advances in perception for self-driving cars have accelerated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under nice weather conditions. Yet, to achieve the high safety requirement, these perceptual systems must operate robustly under a wide variety of weather conditions including snow and rain. In this paper, we present a new dataset to enable robust autonomous driving via a novel data collection process - data is repeatedly recorded along a 15 km route under diverse scene (urban, highway, rural, campus), weather (snow, rain, sun), time (day/night), and traffic conditions (pedestrians, cyclists and cars). The dataset includes images and point clouds from cameras and LiDAR sensors, along with high-precision GPS/INS to establish correspondence across routes. The dataset includes road and object annotations using amodal masks to capture partial occlusions and 3D bounding boxes. We demonstrate the uniqueness of this dataset by analyzing the performance of baselines in amodal segmentation of road and objects, depth estimation, and 3D object detection. The repeated routes opens new research directions in object discovery, continual learning, and anomaly detection. Link to Ithaca365: https://ithaca365.mae.cornell.edu/
