Standard DatasetsCloudy Day Crossroad Dash Cam Video Dataset
Welcome to the fascinating world of autonomous driving, powered by our rich and diverse datasets. These datasets, meticulously curated and annotated, are the lifeblood of the self-driving car industry, fueling advancements across various domains.
The "Cloudy Day Crossroad Dash Cam Video Dataset" specifically captures the intricate dynamics of crossroad navigation under cloudy weather conditions. This dataset is filmed with high-resolution driving recorders, boasting resolutions over 1920 x 1080 pixels and a frame rate of more than 32 fps, to ensure clarity and detail even in subdued lighting. It annotates more than 10 typical urban object categories, including humans, cars, electric bicycles, vans, and trucks, amidst the unique challenges presented at crossroads during cloudy days. The dataset is an essential resource for developing autonomous driving systems capable of understanding and reacting appropriately to complex urban intersections, especially when visibility is affected by overcast skies.
Sample
Specification
Dataset ID
MD-Auto-010
Dataset Name
Cloudy Day Crossroad Dash Cam Video Dataset
Data Type
ImageVolume
About 2.4k annotated images
Data Collection
Driving Recorders Images. Resolution is over 1920 x 1080 and the number of frames per second of the video is over 32.
Annotation
Bounding Box,Tags
Annotation Notes
Total more than 10 typical object categories, such as human, car,electric bicycle,van,truck etc.
Application Scenarios
Autonomous Driving
Data Collections
With approximately 2.4k annotated images, this dataset offers an in-depth view of crossroad scenarios on cloudy days, highlighting the nuances of urban traffic flow and pedestrian movements in less optimal lighting conditions. The detailed annotations and high frame rate videos are crucial for AI models to learn and predict the behavior of various entities at intersections, ensuring safer decision-making processes under varying weather conditions.
Quality Assurance
Relevant Open Datasets
To supplement our Face Parsing Dataset, users can explore these open datasets for additional resources:
Cityscapes Dataset [Learn more]
Focuses on semantic understanding of urban street scenes, featuring semantic, instance-wise, and dense pixel annotations for various classes. It includes 5,000 finely annotated images and 20,000 coarsely annotated images.
Waymo Open Dataset [Learn more]
Offers a high-quality multimodal sensor dataset for autonomous driving extracted from Waymo self-driving vehicles, covering a wide variety of environments and conditions.
nuScenes Dataset [Learn more]
A comprehensive dataset for autonomous driving that enables researchers to study urban driving situations using the full sensor suite of a real self-driving car. The dataset features camera images, lidar sweeps, and detailed map information.
A2D2 Dataset [Learn more]
The Audi Autonomous Driving Dataset (A2D2) offers a large volume of data with various annotations, including semantic segmentation and 3D bounding boxes.
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