Weather & Lighting Datasets for AI Training

Description: The "Cloudy Day City Road Dash Cam Video Dataset" is crafted to address the challenges autonomous driving systems face in overcast weather conditions. Captured with driving recorders at a resolution exceeding 1920 x 1080 pixels and a frame rate of over 31 fps, this dataset ensures detailed visibility even under the diffused lighting of cloudy skies. It includes bounding boxes and tags for more than 10 object categories commonly encountered in urban settings, such as humans, cars, electric bicycles, vans, and trucks. This dataset aims to refine AI models' ability to navigate and make informed decisions in less-than-ideal weather conditions, enhancing safety and reliability.

Description: 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.

Description: The "Low Lighting Dash Cam Video Dataset" is tailored for autonomous driving systems to navigate through low-light conditions, a crucial capability for safe driving during night-time or in poorly lit environments. Captured with driving recorders at resolutions exceeding 1920 x 1080 pixels and a frame rate of more than 30 fps, this dataset focuses on low lighting scenarios across various settings such as crossroads, avenues, and paths. It encompasses bounding boxes and tags for common urban objects like humans, cars, electric bicycles, vans, and trucks, providing a comprehensive view of the challenges faced by autonomous vehicles in reduced visibility.

Description: 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.

Description: The "Sunny Day City Road Dash Cam Video Dataset" captures the vibrant dynamics of city roads under sunny conditions, essential for autonomous driving systems' development. This dataset is recorded with high-resolution driving recorders, boasting over 1920 x 1080 resolution and a frame rate of over 33 fps, ensuring crystal-clear imagery and fluid motion capture. It includes bounding boxes and tags for more than 10 typical urban object categories, including humans, cars, electric bicycles, vans, trucks, and more, providing a rich training ground for AI to recognize and respond to various elements in sunlit urban environments.

Description: The "Sunny Day Crossroads Dash Cam Video Dataset" offers a focused glimpse into the dynamic interactions at crossroads under sunny conditions, a critical aspect of urban driving for autonomous vehicles. Captured with high-resolution driving recorders at over 1920 x 1080 pixels and a frame rate surpassing 34 fps, this dataset provides clear and fluid visuals necessary for detailed analysis and AI training. It encompasses bounding boxes and tags for over 10 typical urban object categories, including humans, cars, electric bicycles, vans, trucks, and more. This rich dataset aims to enhance the decision-making algorithms of self-driving cars at busy intersections, where the complexity of vehicle and pedestrian movements is significantly heightened.