GAMMA: 自動運転のための汎用エージェント運動モデル
GAMMA: A General Agent Motion Model for Autonomous Driving
異種混在する交通エージェントの運動を速度空間での制約付き最適化として予測し、人間の行動状態をパラメータ化することで多様な挙動に対応したリアルタイム予測モデルを提案。
著者: Yuanfu Luo, Panpan Cai, Yiyuan Lee, David Hsu
分類: cs.RO, cs.CV
原文アブストラクト
This paper presents GAMMA, a general motion prediction model that enables large-scale real-time simulation and planning for autonomous driving. GAMMA models heterogeneous, interactive traffic agents. They operate under diverse road conditions, with various geometric and kinematic constraints. GAMMA treats the prediction task as constrained optimization in traffic agents' velocity space. The objective is to optimize an agent's driving performance, while obeying all the constraints resulting from the agent's kinematics, collision avoidance with other agents, and the environmental context. Further, GAMMA explicitly conditions the prediction on human behavioral states as parameters of the optimization model, in order to account for versatile human behaviors. We evaluated GAMMA on a set of real-world benchmark datasets. The results show that GAMMA achieves high prediction accuracy on both homogeneous and heterogeneous traffic datasets, with sub-millisecond execution time. Further, the computational efficiency and the flexibility of GAMMA enable (i) simulation of mixed urban traffic at many locations worldwide and (ii) planning for autonomous driving in dense traffic with uncertain driver behaviors, both in real-time. The open-source code of GAMMA is available online.
