论文标题
学习人行道轨迹预测的行人车辆相互作用
Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Prediction
论文作者
论文摘要
在本文中，我们研究了行人与车辆之间的相互作用，并提出了一种新型的神经网络结构，称为行人车辆相互作用（PVI）提取器，用于学习行人车辆的相互作用。我们在两种顺序方法（长期短期记忆（LSTM）模型）和非序列方法（卷积模型）上实施了建议的PVI提取器。我们使用Waymo打开的数据集，该数据集包含带有行人和车辆注释的真实世界城市交通场景。对于基于LSTM的模型，我们提出的模型与Social-LSTM和Social-Gan进行了比较，使用我们建议的PVI提取器将平均位移误差（ADE）和最终位移误差（FDE）降低了7.46％和5.24％。对于基于卷积的模型，我们提出的模型与社交-STGCNN和Social-IWSTCNN进行了比较，并且使用我们提出的PVI提取器将ADE和FDE降低了2.10％和1.27％。结果表明，行人车的相互作用会影响行人行为，使用拟议的PVI提取器的模型可以捕获行人与车辆之间的相互作用，从而优于比较方法。
In this paper, we study the interaction between pedestrians and vehicles and propose a novel neural network structure called the Pedestrian-Vehicle Interaction (PVI) extractor for learning the pedestrian-vehicle interaction. We implement the proposed PVI extractor on both sequential approaches (long short-term memory (LSTM) models) and non-sequential approaches (convolutional models). We use the Waymo Open Dataset that contains real-world urban traffic scenes with both pedestrian and vehicle annotations. For the LSTM-based models, our proposed model is compared with Social-LSTM and Social-GAN, and using our proposed PVI extractor reduces the average displacement error (ADE) and the final displacement error (FDE) by 7.46% and 5.24%, respectively. For the convolutional-based models, our proposed model is compared with Social-STGCNN and Social-IWSTCNN, and using our proposed PVI extractor reduces the ADE and FDE by 2.10% and 1.27%, respectively. The results show that the pedestrian-vehicle interaction influences pedestrian behavior, and the models using the proposed PVI extractor can capture the interaction between pedestrians and vehicles, and thereby outperform the compared methods.
