Vehicle Turning Intention Prediction Based on Data-Driven Method with Roadside Radar and Vision Sensor

Jyun Hong He, Yen Lin Chen, Xiu Zhi Chen, Hsin Han Chiang

研究成果: 書貢獻/報告類型會議論文篇章

摘要

The trajectory tracking and turning intention prediction of vehicles at intersections are a vital part of smart traffic safety. With the height limitation, traffic objects are often obscured by other vehicles that easily results in blind spots for the visual sensing system. This paper summarizes the author's practice of a roadside unit composed of monitors and radar sensors to track and predict behavioral intentions of traffic objects, and develop a stable system based on the fusion of radar and image sensing information to reduce the danger caused by the steering of other vehicles that are not predicted by the driving sight and the blind angle of the on-board sensor. The roadside unit is installed at the intersection to collect vehicle data on the road, such as position, speed, and direction. An artificial neural network based on LSTM-GAN is used to process data and predict vehicle turning intention. The research case shows that the proposed model has about 91% prediction accuracy.

原文英語
主出版物標題2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665433280
DOIs
出版狀態已發佈 - 2021
事件8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021 - Penghu, 臺灣
持續時間: 2021 9月 152021 9月 17

出版系列

名字2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021

會議

會議8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
國家/地區臺灣
城市Penghu
期間2021/09/152021/09/17

ASJC Scopus subject areas

  • 人工智慧
  • 電腦科學應用
  • 電氣與電子工程
  • 控制和優化
  • 儀器

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