DAT-RNN: Trajectory Prediction with Diverse Attention

Zheng Li, Xiaocong Du, Yu Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Trajectory prediction, an emerging application of spatial-temporal graph, is extremely critical in dynamic applications such as autonomous vehicles and robots. However, the diversity of trajectories and the modeling of mutual relations make it difficult to predict trajectories precisely and efficiently. In this work, we propose a novel approach, diverse attention RNN (DAT-RNN), to handle the diversity of trajectories and the accurate modeling of neighboring relations with two novel and well-designed modules: DAT-RNN first uses a diversity-aware memory (DAM) module, which is based on the detour integral of each individual, to capture the temporal behavior of each person; then DAT-RNN employs an anomaly attention module (AAM), which integrates a weighted sum of spatial relations from multiple neighbors to assist the prediction. With the well-elaborated modules, DAT-RNN integrates both temporal and spatial relations to improve the prediction under various circumstances. Comprehensive experiments on ETH and UCY datasets demonstrate the efficacy of the proposed approach.

Original languageEnglish (US)
Title of host publicationProceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
EditorsM. Arif Wani, Feng Luo, Xiaolin Li, Dejing Dou, Francesco Bonchi
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1512-1518
Number of pages7
ISBN (Electronic)9781728184708
DOIs
StatePublished - Dec 2020
Externally publishedYes
Event19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 - Virtual, Miami, United States
Duration: Dec 14 2020Dec 17 2020

Publication series

NameProceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020

Conference

Conference19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020
Country/TerritoryUnited States
CityVirtual, Miami
Period12/14/2012/17/20

Keywords

  • Recurrent neural network
  • deep learning
  • spatial-temporal graph
  • trajectory prediction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

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