Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas (06-004) [supporting dataset]
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. The 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. The authors anticipate that their 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: (1) LIDAR Point clouds: 10.5K LIDAR Intensity Images; (2) Stereo Images: 5K images with depth maps; (3) Semantic Annotations for Point-Clouds: 10.5K Point-Clouds with point wise labels; (4) Object Detections labels: 5K 2D Bounding Box labels for camera images; and (5) Calibration parameters: Intrinsic and Extrinsic calibration parameters.
- Dataset URL:
- Dataset URL:
- Record URL:
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Supplemental Notes:
- The dataset supports report: Technology to Ensure Equitable Access to Automated Vehicles for Rural Areas, available at the URL above. This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
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Corporate Authors:
Safety Through Disruption University Transportation Center (Safe-D)
Texas A&M Transportation Institute
College Station, TX United StatesOffice of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Authors:
- Ninan, Stephen
- 0000-0003-2543-6700
- Rathinam, Sivakumar
- 0000-0002-9223-7456
- Publication Date: 2023-6-12
Language
- English
Media Info
- Media Type: Dataset
- Dataset: Version: 1.0 Integrity Hash:
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Dataset publisher:
Dataverse
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Subject/Index Terms
- TRT Terms: Autonomous vehicles; Data; Detection and identification systems; Equity; Laser radar; Machine learning; Rural areas
- Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01903407
- Record Type: Publication
- Contract Numbers: 69A3551747115
- Files: UTC, NTL, TRIS, USDOT
- Created Date: Dec 27 2023 10:29AM