Physical AI systems, robots, autonomous vehicles, driver monitoring systems, and in-cabin sensing platforms all need one thing to work: data. Specifically, they need scenario data. And that raises a question every team building these systems has to answer at some point: do you train on synthetic data generated in simulation, or do you collect real-world data from the field?
Neither answer is complete on its own. The right approach depends on your system, your stage of development, and how much data variety you can afford to skip. Let's break it down.
What Is Synthetic Scenario Data?
Synthetic data comes from simulation environments. You define a virtual world, set the rules of physics, place objects, add agents, and generate thousands — sometimes millions — of scenarios your model can train on.
