We present a new algorithm for predicting the near-term trajectories of road-agents in dense traffic videos. Our approach is designed for heterogeneous traffic, where the road-agents may correspond to buses, cars, scooters, bicycles, or pedestrians. We model the interactions between different road-agents using a novel LSTM-CNN hybrid network for trajectory prediction. In particular, we take into account heterogeneous interactions that implicitly accounts for the varying shapes, dynamics, and behaviors of different road agents. In addition, we model horizon-based interactions which are used to implicitly model the driving behavior of each road-agent. We evaluate the performance of our prediction algorithm, TraPHic, on the standard datasets and also introduce a new dense, heterogeneous traffic dataset corresponding to urban Asian videos and agent trajectories. We outperform state-of-the-art methods on dense traffic datasets by 30%.

翻译：我们提出一种新的算法,用于预测密集交通录像中道路试剂的近期轨迹。我们的方法是为不同交通设计一种新的算法,道路试剂可以与公共汽车、汽车、摩托车、自行车或行人相对应。我们用新的LSTM-CNN混合轨道预测网络来模拟不同道路试剂之间的互动。我们特别考虑到各种互动,这些互动暗含不同道路试剂的形状、动态和行为。此外,我们模拟基于地平线的互动,用来隐含地模拟每个道路试剂的驾驶行为。我们用标准数据集来评估我们的预测算法TraPHic的性能,并采用与亚洲城市视频和代理轨迹相对应的新的密集、多式交通数据集。我们比重30%的密集交通数据集的先进方法。