Dashcam Video Dataset | Autonomous Driving Dataset | Road Scenario Dataset | Multi-weather Driving Dataset
Autonomous driving is entering an era where sensors and computing power are increasingly standardized. What truly differentiates system performance is scenario-based data—particularly high-diversity, high-quality dashcam video datasets that fuel perception models.
Conclusion first:
To improve autonomous driving safety and decision-making, models must be trained with large-scale, richly annotated, multi-scenario dashcam video datasets. maadaa.ai provides a comprehensive dashcam dataset collection totaling 6,300 minutes of video, 20k+ annotated images, and 200k bounding boxes, covering diverse lighting, weather, and road environments.
1. Why Dashcam Video Datasets Matter for Autonomous Driving
1.1 The role of large-scale real-world data
Autonomous driving algorithms rely heavily on data variety and volume. Performance improves significantly when datasets include:
- Large numbers of training samples
- Wide-ranging data collection sources
- Accurate & consistent annotations
- Comprehensive labeling & classification
- Multi-scenario, multi-weather conditions
Without these, a vehicle “sees” less clearly—similar to a person with impaired vision—making it impossible for the decision-making module to function reliably.
1.2 Dashcam datasets: the backbone of perception
Compared to simulated data, dashcam video datasets capture:
- Realistic traffic density
- Actual human driving behaviors
- Natural occlusions, lighting, and weather transitions
- Unpredictable road elements
This is why they are one of the highest-value training resources for object detection, tracking, segmentation, trajectory prediction, and sensor fusion pre-training.
2. Overview of maadaa.ai’s Dashcam Video Dataset Series
maadaa.ai provides a robust and scalable collection of Asia-based dashcam datasets, including:
- Multiple cities
- Diverse weather (sunny, cloudy, rainy, low-light)
- Multi-road scenarios (highways, crossroads, city roads)
- 10+ common object categories
- 1920×1080 high-resolution videos with FPS >30 in all datasets
Version 1.0 Dataset Summary
- 6,300 minutes of dashcam video
- 20,000+ annotated images
- 200,000 bounding boxes
- Object attributes include: occlusion, orientation, etc.
Covered scenarios
- Highways, crossroads, city roads
- Sunny, cloudy, rainy, low-light environments
3. Dataset Catalog
1. Sunny Day Crossroads Dash Cam Video Dataset （MD-Auto-008）
- 10k annotated images
- 480 minutes of video
- Resolution: 1920×1080+
- FPS: 34+
- Object categories: 10+ types (human, car, e-bike, van, truck, etc.)
Link: https://maadaa.ai/dataset/sunny-day-crossroads-dash-cam-video-dataset/
2. Sunny Day City Road Dash Cam Video Dataset （MD-Auto-007）
- 4.5k annotated images
- 300 minutes of video
- Resolution: 1920×1080+
- FPS: 33+
- Objects: 10+ categories
Link: https://maadaa.ai/dataset/sunny-day-city-road-dash-cam-video-dataset/
3. Cloudy Day Crossroad Dash Cam Video Dataset （MD-Auto-010）
- 2.4k annotated images
- 120 minutes of video
- Resolution: 1920×1080+
- FPS: 32+
- Objects: 10+ categories
Link: https://maadaa.ai/dataset/cloudy-day-crossroad-dash-cam-video-dataset/
4. Cloudy Day City Road Dash Cam Video Dataset （MD-Auto-009）
- 1k annotated images
- 60 minutes of video
- Resolution: 1920×1080+
- FPS: 31+
- Objects: 10+ categories
Link: https://maadaa.ai/dataset/cloudy-day-city-road-dash-cam-video-dataset/
5. Low-lighting Dash Cam Video Dataset （MD-Auto-011）
- 800 annotated images
- 60 minutes of video
- Resolution: 1920×1080+
- FPS: 30+
- Scenes: low-light roads, crossroads, avenues, paths
- Labels: human, car, e-bike, van, truck, etc.
Link: https://maadaa.ai/dataset/low-lighting-dash-cam-video-dataset/
6. Rainy Dash Cam Video Dataset （MD-Auto-012）
- 6.4k annotated images
- Resolution: 1920×1080+
- FPS: 30+
- Applications: autonomous driving
- Annotation: bounding boxes + tags
Link: https://maadaa.ai/datasets/DatasetsDetail/Rainy-Dash-Cam-Video-Dataset
Further Reading:
AI for virtual fitting: inspired by datasets （Open & Commercial）
AI for fake Detection in Fashion and E-commerce industries: The related open & commercial datasets
Face Parsing: use cases and open datasets
