ベクトル化表現ドリーマー：夢見によるマルチエージェント運動予測
Vectorized Representation Dreamer (VRD): Dreaming-Assisted Multi-Agent Motion-Forecasting
自動運転車の行動を条件として全エージェントの軌道を想像する閉ループ訓練を導入し、マルチエージェント運動予測を行うベクトル化世界モデル手法を提案。Argoverse 2とinDで最先端性能を達成。
著者: Hunter Schofield, Hamidreza Mirkhani, Mohammed Elmahgiubi, Kasra Rezaee, Jinjun Shan
分類: cs.RO, cs.LG
原文アブストラクト
For an autonomous vehicle to plan a path in its environment, it must be able to accurately forecast the trajectory of all dynamic objects in its proximity. While many traditional methods encode observations in the scene to solve this problem, there are few approaches that consider the effect of the ego vehicle's behavior on the future state of the world. In this paper, we introduce VRD, a vectorized world model-inspired approach to the multi-agent motion forecasting problem. Our method combines a traditional open-loop training regime with a novel dreamed closed-loop training pipeline that leverages a kinematic reconstruction task to imagine the trajectory of all agents, conditioned on the action of the ego vehicle. Quantitative and qualitative experiments are conducted on the Argoverse 2 multi-world forecasting evaluation dataset and the intersection drone (inD) dataset to demonstrate the performance of our proposed model. Our model achieves state-of-the-art performance on the single prediction miss rate metric on the Argoverse 2 dataset and performs on par with the leading models for the single prediction displacement metrics.
