How AV Elevate is Revolutionising Autonomous Vehicle Development Through Simulation

Want to speed up autonomous vehicle development? Learn how AV Elevate transforms testing with synthetic training data.

Data screen displaying real-time navigation of an autonomous vehicle speeding along a deserted highway during the day, with clear blue skies and bright sunlight illuminating the dashboard.

An advanced simulation system promises to remove one of the biggest barriers to the progress and ultimate adoption of autonomous vehicles and accelerate their development significantly. The reliance on real-world testing and data collection is one of the biggest barriers to the development of safe, effective, and efficient autonomous vehicles. This is because the painstaking gathering of such data on public roads is an extremely time-consuming, costly, and laborious process.

The advent of a simulation system that enables the tuning of sensor systems, the training of perception and control, offers both closed-loop perception testing, and the creation of 100% accurate synthetic training data is therefore a significant leap forward for the technology. AV Elevate, launched by rFpro, is a fully integrated simulation solution that accelerates the development of autonomous vehicles. It enables the tuning of sensors, the training of perception and control algorithms, and testing of the full AV technology stack. It is the industry's most advanced platform to provide both closed-loop perception testing and the creation of engineering-grade synthetic training data.

Also read: Ansys to demonstrate mobility technology at CES 2025

How AV Elevate Supports Autonomous Vehicle Development 

“AV Elevate is a game-changing simulation platform; it is the most advanced solution to enable the complete AV technology stack to be tuned, trained, and tested,” said Matt Daley, Technical Director at rFpro. “For the first time, AV developers can confidently reduce their reliance on real-world testing, instead subjecting systems to AV Elevate’s highly accurate synthetic data. Now, testing and development can be massively and cost-effectively scaled like never before, removing the biggest barrier to the advancement of autonomous vehicles.”

rFpro provides a simulation environment for the automotive and motorsport industries. It is used for the development and testing of autonomous vehicles, ADAS, vehicle dynamics, and human factor studies. rFpro’s automotive customers are the world’s largest car manufacturers, tier one suppliers, and sensor developers, and it enables them to simulate, test, and validate new sensors, control systems, and vehicle hardware systems. In motorsport, it is the market leader of professional driver-in-the-loop simulator software – with customers including past and present champions of every leading motorsport category.

Key Features of AV Elevate in Autonomous Vehicle Testing

To create AV Elevate, rFpro has integrated several new technologies into its existing platform, including LiDAR, radar, and camera models. The platform also features a new Simulation Manager to simply define the full vehicle sensor suite and create base test scenarios with thousands of iterations. AV Elevate is compatible with High-Performance Computing (HPC) in the cloud, which allows users to conduct and scale testing rapidly.

Tuning Sensor Systems for Autonomous Vehicles AV Elevate integrates high-fidelity sensor models for all major autonomous vehicle sensor types and enables installation choices and configurations to be tuned and optimised. The platform’s synchronous architecture allows for hundreds of sensors to be tested, enabling sensor fusion testing to a level of accuracy not previously possible. Included within the simulation solution is a comprehensive library of standard sensor models alongside digital twins of commercially available sensors. This allows development to progress before a physical sensor exists or enables OEMs to benchmark technologies against their competition.

Synthetic Training Data for Scalable Autonomous Vehicle Testing 

Typically, autonomous vehicle developers manually annotate each frame of video, LiDAR point, or radar return to identify objects in the scene to create training data. This approach generally takes 20 minutes per frame and has a 10% error rate. AV Elevate automates this process using engineering-grade synthetic training data, meaning it is 100 times faster and 150 times more cost-effective than manual annotation and is completely error-free.

The Simulation Manager provides a user-friendly way to set up and execute test scenarios simply and automatically. This allows large-scale simulations with multiple sensor systems to be created with ease. The Simulation Manager can automate variations with both the vehicle model and the environment. For example, changes to the sensor types, positioning of sensors on the vehicle, traffic, pedestrians, time of day, weather conditions, street furniture, and obstructions. It quickly enables the creation of focused variations of the base scenario to be generated, creating hundreds of edge case scenarios for testing. Users can create their own ever-increasing database of scenarios or connect to large external third-party databases.

Transforming Autonomous Vehicle Testing with AV Elevate 

AV Elevate generates and subjects perception and control systems to a massive array of simulated driving scenarios to develop, train, and test them. Due to AV Elevate’s synchronous architecture, the time to complete tests is directly linked to the available computing power. As the platform is cloud and HPC compatible, it allows users to flexibly and rapidly scale testing as required without significant investment in internal computing hardware.

At the core of AV Elevate is rFpro’s industry-recognized physically modeled virtual environments and ray tracing rendering technology. Its library of more than 180 real-world digital twins provides a highly accurate and diverse virtual proving ground, with every element in the scene physically modelled with realistic material characteristics and a road surface model accurate to 1mm. Its ray tracing rendering technology replicates the nuances of how a vehicle’s sensor system perceives the world. The multi-path technique reliably simulates the huge number of light and electromagnetic sources and reflections that happen around a sensor. It generates the highest fidelity data for sensors and includes phenomena such as motion blur and rolling shutter effects.

The Future of Autonomous Vehicle Development with AV Elevate

“The purpose of AV Elevate is to help OEMs reduce their reliance on testing and data collection on public roads to significantly cut costs and accelerate the development of autonomous vehicles,” concludes Daley. “We believe our fully integrated simulation solution is the most accurate available, and this gives manufacturers the confidence needed to make that move.