Social relation and physical lane aggregator:integrating social and physical features for multimodal motion prediction  

作　　者：Qiyuan Chen Zebing Wei Xiao Wang Lingxi Li Yisheng Lv 

机构地区：[1]The State Key Laboratory for Management and Control of Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing,China [2]School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing,China [3]Electrical and Computer Engineering,Indiana University-Purdue University Indianapolis,Indianapolis,Indiana,USA [4]Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing,China and School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing,China

出　　处：《Journal of Intelligent and Connected Vehicles》2022年第3期302-308,共7页智能网联汽车（英文）

基　　金：Chinese Guangdong’s S&T project 2019B1515120030;National Natural Science Foundation of China under Grant 61876011.

摘　　要：Purpose–The purpose of this paper aims to model interaction relationship of traffic agents for motion prediction,which is critical for autonomous driving.It is obvious that traffic agents’trajectories are influenced by physical lane rules and agents’social interactions.Design/methodology/approach–In this paper,the authors propose the social relation and physical lane aggregator for multimodal motion prediction,where the social relations of agents are mainly captured with graph convolutional networks and self-attention mechanism and then fused with the physical lane via the self-attention mechanism.Findings–The proposed methods are evaluated on the Waymo Open Motion Dataset,and the results show the effectiveness of the proposed two feature aggregation modules for trajectory prediction.Originality/value–This paper proposes a new design method to extract traffic interactions,and the attention mechanism is used in each part of the model to extract and fuse different relational features,which is different from other methods and improves the accuracy of the LSTM-based trajectory prediction method.

关 键 词：Deep learning Machine learning Autonomous driving Trajectory prediction 

分 类 号：U49[交通运输工程—交通运输规划与管理] TP39[交通运输工程—道路与铁道工程]