Accurate predictions of future pedestrian trajectory could prevent a considerable number of traffic injuries and improve pedestrian safety. It involves multiple sources of information and real-time interactions, e.g. , vehicle speed and ego-motion, pedestrian intention and historical locations. Existing methods directly apply a simple concatenation operation to combine multiple cues while their dynamics over time are less studied. In this paper, we propose a novel Long Short-Term Memory (LSTM), namely, to incorporate multiple sources of information from pedestrians and vehicles adaptively. Different from LSTM, our considers mutual interactions and explores intrinsic relations among multiple cues. First, we introduce extra memory cells to improve the transferability of LSTMs in modeling future variations. These extra memory cells include a speed cell to explicitly model vehicle speed dynamics, an intention cell to dynamically analyze pedestrian crossing intentions and a correlation cell to exploit correlations among temporal frames. These three individual cells uncover the future movement of vehicles, pedestrians and global scenes. Second, we propose a gated shifting operation to learn the movement of pedestrians. The intention of crossing the road or not would significantly affect pedestrian’s spatial locations. To this end, global scene dynamics and pedestrian intention information are leveraged to model the spatial shifts. Third, we integrate the speed variations to the output gate and dynamically reweight the output channels via the scaling of vehicle speed. The movement of the vehicle would alter the scale of the predicted pedestrian bounding box: as the vehicle gets closer to the pedestrian, the bounding box is enlarging. Our rescaling process captures the relative movement and updates the size of pedestrian bounding boxes accordingly. Experiments conducted on three pedestrian trajectory forecasting benchmarks show that our achieves state-of-the-art performance.

中文翻译：

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用于行人轨迹预测的整体 LSTM


准确预测未来行人轨迹可以防止大量交通伤害并提高行人安全。它涉及多种信息源和实时交互，例如车辆速度和自我运动、行人意图和历史位置。现有方法直接应用简单的串联操作来组合多个线索，但对它们随时间的动态变化的研究较少。在本文中，我们提出了一种新颖的长短期记忆（LSTM），即自适应地合并来自行人和车辆的多种信息源。与 LSTM 不同，我们考虑相互的相互作用并探索多个线索之间的内在关系。首先，我们引入额外的记忆单元来提高 LSTM 在建模未来变化时的可迁移性。这些额外的存储单元包括用于显式建模车辆速度动态的速度单元、用于动态分析行人过路意图的意图单元以及用于利用时间帧之间的相关性的相关单元。这三个独立的单元揭示了车辆、行人和全球场景的未来运动。其次，我们提出了门控换档操作来学习行人的运动。过马路的意图与否会显着影响行人的空间位置。为此，利用全局场景动态和行人意图信息来模拟空间变化。第三，我们将速度变化集成到输出门，并通过车辆速度的缩放动态地重新加权输出通道。车辆的移动会改变预测的行人边界框的比例：当车辆靠近行人时，边界框会扩大。 我们的重新缩放过程捕获相对运动并相应地更新行人边界框的大小。在三个行人轨迹预测基准上进行的实验表明，我们实现了最先进的性能。