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© Qiyuan Chen, Zebing Wei, Xiao Wang, Lingxi Li and Yisheng Lv. This work is published under http://creativecommons.org/licences/by/4.0/legalcode (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

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.

Details

Title
Social relation and physical lane aggregator: integrating social and physical features for multimodal motion prediction
Author
Chen, Qiyuan 1 ; Zebing Wei 1 ; Wang, Xiao 1 ; Li, Lingxi 2 ; Lv, Yisheng 3 

 The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China and School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 
 Electrical and Computer Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, USA 
 Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China and School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China 
Pages
302-308
Publication year
2022
Publication date
2022
Publisher
Emerald Group Publishing Limited
e-ISSN
23999802
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2779933161
Copyright
© Qiyuan Chen, Zebing Wei, Xiao Wang, Lingxi Li and Yisheng Lv. This work is published under http://creativecommons.org/licences/by/4.0/legalcode (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.