Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023

• URL: http://arxiv.org/abs/2306.11868v1
• Date: Tue, 20 Jun 2023 20:01:07 GMT
• Title: Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023
• Authors: Yu Wang, Tiebiao Zhao, Fan Yi
• Abstract summary: This report presents our 1st place solution for the Open Sim Agents Challenge (WOSAC) 2023. Our proposed MultiVerse Transformer for Agent simulation (MVTA) effectively leverages transformer-based motion prediction approaches. In order to produce simulations with a high degree of realism, we design novel training and sampling methods.
• Score: 3.4520774137890555
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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