The largest cost of developing artificial intelligence-based automated driving solutions is collecting and labelling data for training and validation regardless of autonomy level. Furthermore, data quality and diversity are also critical to enable truly robust and intelligent systems.

The use of synthetic and augmented data coupled with automatically annotated real-world data will be a game-changer for developing, testing and updating the next generation of Automated Driving software solutions.

This talk will discuss state-of-the-art data generation and labelling methods, introduce an integrated, cost-efficient, data-driven pipeline, and use different hardware platforms at different stages, from training to in-vehicle integration.