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Advances in perception for self-driving cars have accelerated in recent yearsdue to the availability of large-scale datasets, typically collected atspecific locations and under nice weather conditions. Yet, to achieve the highsafety requirement, these perceptual systems must operate robustly under a widevariety of weather conditions including snow and rain. In this paper, wepresent a new dataset to enable robust autonomous driving via a novel datacollection process - data is repeatedly recorded along a 15 km route underdiverse scene (urban, highway, rural, campus), weather (snow, rain, sun), time(day/night), and traffic conditions (pedestrians, cyclists and cars). Thedataset includes images and point clouds from cameras and LiDAR sensors, alongwith high-precision GPS/INS to establish correspondence across routes. Thedataset includes road and object annotations using amodal masks to capturepartial occlusions and 3D bounding boxes. We demonstrate the uniqueness of thisdataset by analyzing the performance of baselines in amodal segmentation ofroad and objects, depth estimation, and 3D object detection. The repeatedroutes opens new research directions in object discovery, continual learning,and anomaly detection. Link to Ithaca365: https://ithaca365.mae.cornell.edu/

Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions