Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous
  Driving Research
Cole Gulino Justin Fu Wenjie Luo George Tucker Eli Bronstein Yiren Lu Jean Harb Xinlei Pan Yan Wang Xiangyu Chen John D. Co-Reyes Rishabh Agarwal Rebecca Roelofs Yao Lu Nico Montali Paul Mougin Zoey Yang Brandyn White Aleksandra Faust Rowan McAllister Dragomir Anguelov Benjamin Sapp
Simulation is an essential tool to develop and benchmark autonomous vehicle
planning software in a safe and cost-effective manner. However, realistic
simulation requires accurate modeling of nuanced and complex multi-agent
interactive behaviors. To address these challenges, we introduce Waymax, a new
data-driven simulator for autonomous driving in multi-agent scenes, designed
for large-scale simulation and testing. Waymax uses publicly-released,
real-world driving data (e.g., the Waymo Open Motion Dataset) to initialize or
play back a diverse set of multi-agent simulated scenarios. It runs entirely on
hardware accelerators such as TPUs/GPUs and supports in-graph simulation for
training, making it suitable for modern large-scale, distributed machine
learning workflows. To support online training and evaluation, Waymax includes
several learned and hard-coded behavior models that allow for realistic
interaction within simulation. To supplement Waymax, we benchmark a suite of
popular imitation and reinforcement learning algorithms with ablation studies
on different design decisions, where we highlight the effectiveness of routes
as guidance for planning agents and the ability of RL to overfit against
simulated agents.