DeepUbuntuAV
Real-world autonomous vehicle datasets focusing on identifying, simulating and addressing edge cases.
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Why DeepUbuntuAV?
While other companies react to edge cases after they cause failures, we anticipate, simulate and address them before they hit your fleet. Our neural networks analyze traffic patterns, weather data, and infrastructure changes to predict which scenarios will challenge autonomous systems. We don't just collect diverse data, we predict problems before they happen and generate comprehensive training environments for scenarios your vehicles haven't encountered yet.
Predictive Edge Case Engine
Most AV companies react to edge cases after they cause failures. We anticipate, simulate and address them before they hit your fleet. Our neural networks analyze traffic patterns, weather data, and infrastructure changes to predict which scenarios will challenge autonomous systems in the next 6-12 months.
Rapid Scenario Multiplication
When a new edge case emerges, like a construction pattern or unusual pedestrian behavior, we generate thousands of realistic variations within hours. This means your models train on comprehensive scenario families instead of single isolated incidents, dramatically improving generalization to unseen situations.
Standards-Based Integration
Built on OpenDRIVE road networks, SUMO traffic simulation, and ROS sensor frameworks. Your existing training pipelines work with our data immediately. No custom parsers, no format conversions, no infrastructure rewrites. We speak the same technical language your teams already use.
Proactive Training Datasets
Instead of waiting for real-world failures to generate training data, we create comprehensive datasets for scenarios your vehicles haven't encountered yet. This includes weather combinations, traffic densities, and infrastructure states that would take years to collect naturally through fleet operations.
Regulatory Scenario Modeling
We model traffic law changes, new road designs, and emerging vehicle technologies before they become widespread. When new regulations or infrastructure patterns roll out globally, your models are already trained and ready, giving you a significant deployment advantage.
Failure Pattern Analysis
Our systems identify the underlying patterns behind AV failures across different companies and regions. We don't just recreate individual incidents, we understand why certain scenario types consistently challenge autonomous systems and build comprehensive training environments around those failure modes.