Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents
  Challenge 2023
Yu Wang Tiebiao Zhao Fan Yi
This technical report presents our 1st place solution for the Waymo Open Sim
Agents Challenge (WOSAC) 2023. Our proposed MultiVerse Transformer for Agent
simulation (MVTA) effectively leverages transformer-based motion prediction
approaches, and is tailored for closed-loop simulation of agents. In order to
produce simulations with a high degree of realism, we design novel training and
sampling methods, and implement a receding horizon prediction mechanism. In
addition, we introduce a variable-length history aggregation method to mitigate
the compounding error that can arise during closed-loop autoregressive
execution. On the WOSAC, our MVTA and its enhanced version MVTE reach a realism
meta-metric of 0.5091 and 0.5168, respectively, outperforming all the other
methods on the leaderboard.