Simulation City: Introducing Waymo's most advanced simulation system yet for autonomous driving
To answer this question, Waymo created a software solution internally dubbed SimulationCity to add to our industry-leading evaluation tools. SimulationCity automatically synthesizes entire journeys to assess the Waymo Driver's performance – whether it's safely completing a 20-minute Waymo One rider-only trip across San Francisco or efficiently running an 11-hour goods-only Waymo Via trip from Phoenix to Dallas. The capability is enabled by the more than 20 million autonomous miles the Waymo Driver has collected on public roads, third-party data such as NHTSA Crash Data Systems and Naturalistic Driving Study Data, Waymo's unique access to Alphabet's state-of-art technical infrastructure, and the culmination of advancements in sensor simulation and machine learning techniques in areas such as intelligent agents, object trajectory generation, and high-quality automated data labeling. With the power of SimulationCity, we can significantly accelerate the development and deployment of the Waymo Driver – an “everything engine” that allows us to safely bring up new vehicle platforms, test them in new geographies and refine our operations so we can bring our technology to more people in more places. Let's back up and see how it has all come together.
For simulation to be an effective tool, it has to closely represent the environment it's emulating to provide accurate insights into a system’s performance. What happens in simulation must be predictive of what happens in the real world to ensure you are simulating the right things. This requires minimizing the differences between the simulated and real world, from the way your sensors see to the way your agents react to changes in their simulated environments — and are statistically representative of the real world.
