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Autonomous Vehicle Simulation
Explore high-fidelity and diverse sensor simulation for safe autonomous vehicle development.
Workloads
Simulation / Modeling / Design
Industries
Automotive and Transportation
Business Goal
Return on Investment
Risk Mitigation
Products
NVIDIA Omniverse Enterprise
NVIDIA OVX
NVIDIA DGX
Overview
The Need for High-Fidelity AV Simulation
Developing autonomous vehicles (AVs) requires vast amounts of training data that mirrors the real-world conditions they’ll face on the road. Sensor simulation addresses this challenge by rendering physically-based sensor data in virtual environments. Conditioned on these physics, world foundation models add variation to sensor simulation, amplifying lighting, weather, geolocations, and more. With these capabilities, you can train, test, and validate AVs at scale without having to encounter rare and dangerous scenarios in the real world. The precision and diversity in sensor data and environmental interaction are crucial for developing physical AI.
Why AV Simulation Matters:
Safety
Render diverse driving conditions—such as adverse weather, traffic changes, and rare or dangerous scenarios—without having to encounter them in the real world.
Cost Efficiency
Accelerate development and reduce reliance on costly data-collection fleets by generating data to meet model needs.
Scalability and Flexibility
Deploy a virtual fleet to prototype new sensors and stacks before physical prototyping.
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Technical Implementation
Running Physically Accurate AV Simulation At Scale
The NVIDIA Omniverse™ Blueprint for Autonomous Vehicle (AV) Simulation is a reference workflow to create rich 3D worlds for training, testing, and validation. The blueprint contains APIs and services to build and enhance digital twins from real-world sensor data, model physics and behavior of dynamic objects in a scene, and generate physically accurate and diverse sensor data.
With this API-based architecture, the blueprint can be seamlessly integrated into existing workflows, enabling developers to replay driving data, generate new ground-truth data, and perform closed-loop testing.
The blueprint is part of NVIDIA Halos guardian framework for AV safety, which comprises state-of-the-art HW/SW elements, tools, models, and design principles to safeguard end-to-end AV stacks, from the cloud to the car.
The Omniverse Blueprint for AV Simulation includes:
- Neural Reconstruction: Enhances real-world driving logs with novel sensor angles and asset addition/removal.
- Sensor RTX: Renders high-fidelity, physically-based sensor data for sensors commonly used for autonomy, including camera, radar, and lidar.
- AV Sim Enhancement: Provides the physics, behavior, and animation necessary to simulate real-world scenarios.
- Cosmos Transfer WFM: Generates new variations in a given scenario, including weather, lighting, and geography, based on physics provided in Omniverse.
Foretellix
Autonomous Vehicle Sensor Simulation, Powered by NVIDIA Omniverse
See how Foretellix uses NVIDIA Omniverse Blueprint for AV Simulation to generate high-fidelity sensor simulation for autonomous vehicle development.
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Partners
Get Started With AV Simulation
Learn how our partners are delivering physically-based simulation for safe and efficient autonomous vehicle development.