一种联合时空信息与社交互动特征的行人轨迹预测方法

A pedestrian trajectory prediction method intergrating spatiotemporal information and social interaction features

针对目前基于数据驱动的建模方法,难以有效表达和综合行人时序运动特征及行人间复杂抽象的社交互动行为的问题,本文提出了一种联合时空信息和社交互动特征的行人轨迹预测方法。首先,对行人历史运动轨迹编码得到行人的运动特征;其次,结合长短期记忆网络和特征注意力机制,捕获行人自我运动序列的时空关联信息;再者,在时序特征提取基础上,使用图卷积网络建模行人间的社交互动特征;最后,利用多模态未来轨迹解码模块预测行人的多模态未来运动轨迹,并采用ETH、UCY数据集对所提出的模型进行评价分析。结果表明,本方法具备有效性,能够稳健可靠地实现行人轨迹预测。

social interaction behaviors among pedestrians.To address these challenges,this paper proposes a pedestrian trajectory prediction method that integrates spatiotemporal information and social interaction features.Firstly,historical pedestrian trajectories are obtained,and a motion trajectory mapping module based on a multi-layer perceptron is used to encode preliminary pedestrian historical trajectory information.Then,based on a combination of long short-term memory(LSTM)networks and feature attention mechanisms,a motion spatiotemporal feature encoding module is designed to explore the temporal dependency of pedestrian self-motion sequences within the observation period and selectively capture the spatiotemporal correlation information of pedestrian self-motion sequences.Furthermore,based on the analysis of the complex interactions between pedestrian self-motion and surrounding pedestrians,a pedestrian social interaction information propagation module based on graph convolutional networks(GCN)is introduced to model the social interaction features among pedestrians within the same scene.Finally,leveraging a multi-modal future trajectory decoding module incorporating the concept of Laplace mixture distribution,the integrated analysis and decoding of pedestrian historical trajectory spatiotemporal correlation information and social interaction features are performed to predict trajectory distributions and capture the uncertainty of future trajectories,resulting in multiple future motion trajectories for pedestrians.The proposed model is qualitatively and quantitatively analyzed using the ETH and UCY public datasets in this paper.Average displacement error(ADE)and final displacement error(FDE)are selected to evaluate the performance of the network model on the ETH and UCY datasets.The experimental results are compared with traditional methods and several current mainstream methods.Compared with the optimal models among them,the proposed model reduces ADE and FDE by 2%and 5%,respectively.The experimental results demonstrate that the proposed method can robustly and reliably achieve pedestrian trajectory prediction,and extensive comparative experiments also confirm the effectiveness of the proposed approach.