DeepAV
Autonomous vehicle dataset platform
Autonomous Pipeline
- •End-to-end automation from raw data to training-ready datasets
- •AI-driven quality control and consistency validation
- •Continuous improvement through feedback loops
- •Seamless integration with existing ML workflows
Built for production autonomy
High-quality training data across diverse weather conditions, challenging road scenarios, and edge cases that matter
Sensor Coverage
Multi-modal data from cameras, LiDAR, radar, and GPS across diverse environments
Weather Conditions
Rain, snow, fog, night driving, and challenging lighting conditions
Edge Cases
Rare scenarios, construction zones, emergency vehicles, and unusual road conditions
Precision Labeling
AI-assisted annotation tools that achieve 98%+ accuracy for 2D/3D bounding boxes, semantic segmentation, and object tracking
Intelligent Curation
Automated quality scoring and filtering that surfaces the most valuable training examples while removing redundant or low-quality data
Model Evaluation
Comprehensive performance metrics across scenario types, weather conditions, and edge cases with detailed failure analysis
Continuous Pipeline
Seamless integration with your existing ML infrastructure through standard formats (KITTI, nuScenes, Waymo Open Dataset)
Technical specifications
Built for every stage
Research & Development
Accelerate perception model development with diverse, high-quality datasets covering rare scenarios your test fleet hasn't encountered yet
Testing & Validation
Validate model performance across comprehensive scenario coverage before deploying to physical test vehicles
Production Training
Scale your model training with production-grade data pipelines and continuous dataset expansion
Safety Analysis
Analyze failure modes and edge cases with detailed annotations and metadata for safety-critical scenarios