Accelerating AV Training Data and Testing

Decoupling from real-time data collection saves time and cost while adding flexibility and quality.

Generating reliable training data to support deep learning for autonomous vehicle (AV) artificial intelligence via real-world recorded scenarios can be expensive, time inefficient and inflexible. Driving simulation specialist rFpro wants that to change. The company has developed a new approach - de-coupled from real time - that it claims delivers more effective, cost efficient and accelerated AV training and testing.

The new approach significantly reduces hardware costs, said Matt Daley, rFpro's managing director. He said the industry needs to generate high-quality, simulated training data that can complement existing real-world recorded data. “Achieving the necessary quality is notoriously challenging,” Daley told SAE International. “Delivering dependable training data requires several key components, such as the vehicle model, sensor models, traffic simulator and the digital world content to work together, plus a robust simulation process to harness the available computing power.”