Trajectory of Prediction of Immediate Surroundings for Autonomous Vehicles Using Hierarchical Deep Learning Model

Pei Yun Hsu, Mei Lin Huang, Hsin Han Chiang*

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

摘要

A predicting model based on long-short-term-memory (LSTM) and gated recurrent unit (GRU) is proposed to assist autonomous vehicles (AVs) to drive safely. To understand the behaviors of surroundings under a mixed scene of vehicles, bicycles, and pedestrians, the proposed model can predict the future trajectory of each object with models constructed by GRU. Since different objects have diverse behaviors, this paper applies different models to different categories for vehicles, pedestrians, and cyclists. For each object, the proposed model considers three observed trajectories with different time steps as the input data to predict a more accurate future trajectory. The proposed model is verified and compared with LSTM and GRU on KITTI dataset in the conducted experiments.

原文英語
主出版物標題2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020
編輯Teen-Hang Meen
發行者Institute of Electrical and Electronics Engineers Inc.
頁面263-266
頁數4
ISBN(電子)9781728180601
DOIs
出版狀態已發佈 - 2020 十月 23
事件2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020 - Yunlin, 臺灣
持續時間: 2020 十月 232020 十月 25

出版系列

名字2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020

會議

會議2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020
國家/地區臺灣
城市Yunlin
期間2020/10/232020/10/25

ASJC Scopus subject areas

  • 人工智慧
  • 電腦網路與通信
  • 電腦科學應用
  • 訊號處理
  • 生物醫學工程
  • 電氣與電子工程
  • 控制和優化
  • 儀器

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