ReCoAt: 自動運転のためのマルチモーダル軌道予測ディープラーニングフレームワーク
ReCoAt: A Deep Learning-based Framework for Multi-Modal Motion Prediction in Autonomous Driving Application
RNN・CNN・距離ベース注意機構を組み合わせ、周囲のエージェントの将来軌道を複数モードで予測する手法を提案し、Waymoデータセットで高精度を示した。
著者: Zhiyu Huang, Xiaoyu Mo, Chen Lv
分類: cs.RO
原文アブストラクト
This paper proposes a novel deep learning framework for multi-modal motion prediction. The framework consists of three parts: recurrent neural networks to process the target agent's motion process, convolutional neural networks to process the rasterized environment representation, and a distance-based attention mechanism to process the interactions among different agents. We validate the proposed framework on a large-scale real-world driving dataset, Waymo open motion dataset, and compare its performance against other methods on the standard testing benchmark. The qualitative results manifest that the predicted trajectories given by our model are accurate, diverse, and in accordance with the road structure. The quantitative results on the standard benchmark reveal that our model outperforms other baseline methods in terms of prediction accuracy and other evaluation metrics. The proposed framework is the second-place winner of the 2021 Waymo open dataset motion prediction challenge.
