Part 5/13:
Tesla's use of simulation stands out as a pivotal element. By creating synthetic data—virtual intersections, varied environmental scenarios, or unpredictable behaviors—Tesla can augment real-world data, testing scenarios that are rare or dangerous to capture naturally. These simulations serve multiple purposes: generating training data, validating models, and refining decision algorithms.
Philbert highlights the importance of high-fidelity physics-based environments that approximate real-world physics indistinguishably. Through simulation, Tesla can repeatedly challenge AI systems with edge cases—like vehicles jumping lanes or sudden pedestrian appearances—bolstering robustness.