Pedestrian trajectory prediction is a critical yet challenging task, especially for crowded scenes. We suggest that introducing an attention mechanism to infer the importance of different neighbors is critical for accurate trajectory prediction in scenes with varying crowd size. In this work, we propose a novel method, AVGCN, for trajectory prediction utilizing graph convolutional networks (GCN) based on human attention (A denotes attention, V denotes visual field constraints). First, we train an attention network that estimates the importance of neighboring pedestrians, using gaze data collected as subjects perform a bird's eye view crowd navigation task. Then, we incorporate the learned attention weights modulated by constraints on the pedestrian's visual field into a trajectory prediction network that uses a GCN to aggregate information from neighbors efficiently. AVGCN also considers the stochastic nature of pedestrian trajectories by taking advantage of variational trajectory prediction. Our approach achieves state-of-the-art performance on several trajectory prediction benchmarks, and the lowest average prediction error over all considered benchmarks.

翻译：在这项工作中,我们提出了一个新的轨迹预测方法,即AVGCN, 利用以人类关注为基础的图形革命网络(GCN)进行轨迹预测(表示注意,V表示视场限制)。首先,我们培训一个关注网络,利用收集的视像数据来估计相邻行人的重要性,作为鸟类眼视导航任务的对象。然后,我们将行人视场受限制而得出的关注权重纳入一个轨迹预测网络,利用GCN高效率地从邻居那里收集信息。AVGCN还利用变化轨迹预测,考虑行人轨轨迹的随机性。我们的方法在一些轨迹预测基准上取得了最先进的表现,在所有考虑的基准上平均预测误差最小。