Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas (06-004)

Project Description: The role of multimodal sensor datasets in training autonomous vehicle machine learning algorithms is crucial. While there are several existing datasets available, the majority of them focus on urban road scenarios. This paper introduces the Rural Road Detection Dataset (R2D2), which aims to overcome this limitation by providing a comprehensive collection of labeled point clouds specifically for object detection and semantic segmentation of rural roads. Our dataset encompasses diverse rural environments and road types, creating a challenging learning environment for machine learning algorithms. With over 10,000 labeled point clouds obtained from various locations, R2D2 serves as a valuable resource for researchers and practitioners working towards safer and more efficient transportation systems in rural areas. We anticipate that our dataset will expedite the progress of autonomous driving in remote regions, bringing us closer to a future where all roads, regardless of their rural nature, can be navigated with safety and efficiency. Data Scope: The dataset contains: LIDAR Poinclouds: 10.5K LIDAR Intensity Images Stereo Images: 5K images with depth maps Semantic Annotations for Point-Clouds: 10.5K Point-Clouds with point wise labels. Object Detections labels: 5K 2D Bounding Box labels for camera images Calibration parameters: Intrinsic and Extrinsic calibration parameters Data Specification: Please refer attached file "R2D2_Specifications.pdf"