Parallel Domain says autonomous driving won’t scale without synthetic data
Achieving autonomous driving safely requires near endless hours of training software on every situation that could possibly arise before putting a vehicle on the road. Historically, autonomy companies have collected hordes of real-world data with which to train their algorithms, but it’s impossible to train a system how to handle edge cases based on real-world data alone. Not only that, but it’s time-consuming to even collect, sort and label all that data in the first place.