Simulation is an invaluable tool in the quest for autonomous driving. It allows closed-loop training and testing that covers rare modalities, has perfect ground-truth labels, and can rapidly and flexibly generate millions of scenarios. However, to ensure that policies learned and evaluated in simulation generalize to real-world performance, we must close the discrepancies between the two. In this talk, we will discuss some of the ways in which we leverage Tesla's billions of miles of driving data to improve the realism and diversity of our simulator (from automatic content creation to neural rendering), and how we use this simulator to yield results in the real-world.
Landon Smith is the Lead Machine Learning Scientist on Tesla's Autopilot Simulation team. His work focuses on generative, adversarial scenario design, and actor modeling; exploring the ability of Machine Learning not just to describe the real world, but to replicate it. Previously he founded Strada Routing, a trucking logistics company using ML to solve complex route optimization problems. He is pursuing his Bachelor's at Harvard, where he studies Math, Computer Science, and Molecular Biology.