Vehicle trajectory prediction based on social generative adversarial network for self-driving car applications

Li Wei Kang, Chih Chung Hsu*, I. Shan Wang, Ting Lei Liu, Shih Yu Chen, Chuan Yu Chang

*Corresponding author for this work

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

Abstract

Self-driving or autonomous vehicles need to efficiently and continuously navigate in complex traffic environments by analyzing the surrounding scene, understanding the behavior of other traffic-agents, and predicting their future trajectories. The main goal is to plan a safe motion and reduce the reaction time for possibly imminent hazards. A critical and challenging problem considered in this paper is to explore the movement patterns of surrounding traffic-agents and accurately predict their future trajectories for helping the vehicle make reasonable decision. To solve the problem, a deep learning-based framework is proposed in this paper for predicting trajectories of autonomous vehicles. The key is to train a social GAN (generative adversarial network) deep model for prediction of vehicle trajectory. The presented experimental results have verified that the proposed social GAN-based approach outperforms the traditional Social LSTM (long short-term memory)-based method.

Original languageEnglish
Title of host publicationProceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages489-492
Number of pages4
ISBN (Electronic)9781728193625
DOIs
Publication statusPublished - 2020 Nov
Event2020 International Symposium on Computer, Consumer and Control, IS3C 2020 - Taichung, Taiwan
Duration: 2020 Nov 132020 Nov 16

Publication series

NameProceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020

Conference

Conference2020 International Symposium on Computer, Consumer and Control, IS3C 2020
Country/TerritoryTaiwan
CityTaichung
Period2020/11/132020/11/16

Keywords

  • Autonomous vehicles
  • Deep learning
  • Generative adversarial network
  • Self-driving
  • Vehicle trajectory

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Optimization
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Computer Science Applications
  • Energy Engineering and Power Technology
  • Artificial Intelligence

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