動的環境における複数エージェントのロバスト軌道予測
Robust Trajectory Forecasting for Multiple Intelligent Agents in Dynamic Scene
動的シーンで相互作用する複数エージェントの軌道予測のため、相互作用ネット・環境ネット・予測ネットを組み合わせた手法を提案し、歩行者や車両のデータセットで高精度を達成した。
著者: Yanliang Zhu, Dongchun Ren, Mingyu Fan, Deheng Qian, Xin Li, Huaxia Xia
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Trajectory forecasting, or trajectory prediction, of multiple interacting agents in dynamic scenes, is an important problem for many applications, such as robotic systems and autonomous driving. The problem is a great challenge because of the complex interactions among the agents and their interactions with the surrounding scenes. In this paper, we present a novel method for the robust trajectory forecasting of multiple intelligent agents in dynamic scenes. The proposed method consists of three major interrelated components: an interaction net for global spatiotemporal interactive feature extraction, an environment net for decoding dynamic scenes (i.e., the surrounding road topology of an agent), and a prediction net that combines the spatiotemporal feature, the scene feature, the past trajectories of agents and some random noise for the robust trajectory prediction of agents. Experiments on pedestrian-walking and vehicle-pedestrian heterogeneous datasets demonstrate that the proposed method outperforms the state-of-the-art prediction methods in terms of prediction accuracy.
