Canadian Adverse Driving Conditions dataset
| Autor: | Danson Evan Garcia, Jason Rebello, Carlos Wang, Steven L. Waslander, Matthew Pitropov, Michael Smart, Krzysztof Czarnecki |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Meteorology business.industry Computer Vision and Pattern Recognition (cs.CV) Applied Mathematics Mechanical Engineering Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 020901 industrial engineering & automation Lidar Artificial Intelligence Inertial measurement unit Modeling and Simulation 0202 electrical engineering electronic engineering information engineering Global Positioning System Environmental science 020201 artificial intelligence & image processing Electrical and Electronic Engineering business Software Winter weather |
| Zdroj: | The International Journal of Robotics Research. 40:681-690 |
| ISSN: | 1741-3176 0278-3649 |
| DOI: | 10.1177/0278364920979368 |
| Popis: | The Canadian Adverse Driving Conditions (CADC) dataset was collected with the Autonomoose autonomous vehicle platform, based on a modified Lincoln MKZ. The dataset, collected during winter within the Region of Waterloo, Canada, is the first autonomous driving dataset that focuses on adverse driving conditions specifically. It contains 7,000 frames of annotated data from 8 cameras (Ximea MQ013CG-E2), lidar (VLP-32C), and a GNSS+INS system (Novatel OEM638), collected through a variety of winter weather conditions. The sensors are time synchronized and calibrated with the intrinsic and extrinsic calibrations included in the dataset. Lidar frame annotations that represent ground truth for 3D object detection and tracking have been provided by Scale AI. |
| Databáze: | OpenAIRE |
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