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Rainy 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 "Rainy Dash Cam Video Dataset" is specifically developed for autonomous driving systems to accurately function under rainy conditions, which pose significant visibility and surface traction challenges. Captured with driving recorders at resolutions exceeding 1920 x 1080 pixels and a frame rate of more than 30 fps, this dataset focuses on rainy day scenarios in urban settings, including crossroads, avenues, and paths. It features bounding boxes and tags for over 10 common urban categories such as humans, cars, electric bicycles, vans, and trucks, under the variable and often difficult lighting conditions that accompany rainy weather.

Sample

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Specification

Dataset ID
MD-Auto-012
Dataset Name
Rainy Dash Cam Video Dataset
Data Type
data-typeImage
Volume
About 6.4k 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 30.
Annotation
Bounding Box,Tags
Annotation Notes
Mainly from rainy days, crossroad,avenues and paths as the main scene. The labels include human, car,electric bicycle,van,truck etc.
Application Scenarios
Autonomous Driving

Data Collections

With around 6.4k annotated images, this dataset provides a comprehensive look at the operational challenges faced by autonomous vehicles during rainfall. The videos capture the dynamic nature of urban traffic and pedestrian movements amidst rain, highlighting the critical need for sophisticated detection and navigation systems that can adapt to wet road conditions and reduced visibility. This dataset is an essential tool for enhancing the all-weather capabilities of autonomous driving technologies.

Quality Assurance

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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