Traffic Agent Trajectory Prediction Using a Time Sequence Deep Learning Model with Trajectory Mapping for Autonomous Driving

Pei Yun Hsu; Mei Lin Huang; Wei Yen Wang; Hsin Han Chiang

doi:10.1109/ICCE-TW52618.2021.9603003

Traffic Agent Trajectory Prediction Using a Time Sequence Deep Learning Model with Trajectory Mapping for Autonomous Driving

Pei Yun Hsu, Mei Lin Huang, Wei Yen Wang, Hsin Han Chiang

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

The trajectory prediction of traffic agents plays an important role regarding to the safety of autonomous driving. Structured by gate recurrent unit (GRU), this paper proposes a new predict model with the combination of trajectory mapping method. The experimental results show that the proposed model can feasibly predict the future trajectories of the surrounding traffic agents in a mixed flow including vehicles, cyclists, and pedestrians.

Original language	English
Title of host publication	2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665433280
DOIs	https://doi.org/10.1109/ICCE-TW52618.2021.9603003
Publication status	Published - 2021
Event	8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021 - Penghu, Taiwan Duration: 2021 Sept 15 → 2021 Sept 17

Publication series

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

Conference

Conference	8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021
Country/Territory	Taiwan
City	Penghu
Period	2021/09/15 → 2021/09/17

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Electrical and Electronic Engineering
Control and Optimization
Instrumentation

Access to Document

10.1109/ICCE-TW52618.2021.9603003

Cite this

Hsu, P. Y., Huang, M. L., Wang, W. Y., & Chiang, H. H. (2021). Traffic Agent Trajectory Prediction Using a Time Sequence Deep Learning Model with Trajectory Mapping for Autonomous Driving. In 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021 (2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-TW52618.2021.9603003

Traffic Agent Trajectory Prediction Using a Time Sequence Deep Learning Model with Trajectory Mapping for Autonomous Driving. / Hsu, Pei Yun; Huang, Mei Lin; Wang, Wei Yen et al.
2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hsu, PY, Huang, ML, Wang, WY & Chiang, HH 2021, Traffic Agent Trajectory Prediction Using a Time Sequence Deep Learning Model with Trajectory Mapping for Autonomous Driving. in 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021. 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021, Institute of Electrical and Electronics Engineers Inc., 8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021, Penghu, Taiwan, 2021/09/15. https://doi.org/10.1109/ICCE-TW52618.2021.9603003

Hsu PY, Huang ML, Wang WY , Chiang HH. Traffic Agent Trajectory Prediction Using a Time Sequence Deep Learning Model with Trajectory Mapping for Autonomous Driving. In 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021). doi: 10.1109/ICCE-TW52618.2021.9603003

Hsu, Pei Yun ; Huang, Mei Lin ; Wang, Wei Yen et al. / Traffic Agent Trajectory Prediction Using a Time Sequence Deep Learning Model with Trajectory Mapping for Autonomous Driving. 2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021).

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abstract = "The trajectory prediction of traffic agents plays an important role regarding to the safety of autonomous driving. Structured by gate recurrent unit (GRU), this paper proposes a new predict model with the combination of trajectory mapping method. The experimental results show that the proposed model can feasibly predict the future trajectories of the surrounding traffic agents in a mixed flow including vehicles, cyclists, and pedestrians.",

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Traffic Agent Trajectory Prediction Using a Time Sequence Deep Learning Model with Trajectory Mapping for Autonomous Driving

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