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.
Cite
@article{arxiv.2306.11868,
title = {Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023},
author = {Yu Wang and Tiebiao Zhao and Fan Yi},
journal= {arXiv preprint arXiv:2306.11868},
year = {2023}
}
Comments
Technical report for the 1st place solution of Waymo Open Sim Agents Challenge 2023. Project page: https://multiverse-transformer.github.io/sim-agents/. CVPR 2023 workshop on Autonomous Driving: https://cvpr2023.wad.vision/