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

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面489-492
頁數4
ISBN(電子)9781728193625
DOIs
出版狀態Published - 2020 十一月
事件2020 International Symposium on Computer, Consumer and Control, IS3C 2020 - Taichung, Taiwan
持續時間: 2020 十一月 132020 十一月 16

出版系列

名字Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020

Conference

Conference2020 International Symposium on Computer, Consumer and Control, IS3C 2020
國家/地區Taiwan
城市Taichung
期間20-11-1320-11-16

All Science Journal Classification (ASJC) codes

  • 電氣與電子工程
  • 控制和優化
  • 儀器
  • 原子與分子物理與光學
  • 電腦科學應用
  • 能源工程與電力技術
  • 人工智慧

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