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

Pei Yun Hsu; Mei Lin Huang; Hsin Han Chiang

doi:10.1109/ECICE50847.2020.9301976

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

Pei Yun Hsu
, Mei Lin Huang
, Hsin Han Chiang

Department of Vehicle Engineering

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

2 Scopus citations

Abstract

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.

Original language	English
Title of host publication	2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020
Editors	Teen-Hang Meen
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	263-266
Number of pages	4
ISBN (Electronic)	9781728180601
DOIs	https://doi.org/10.1109/ECICE50847.2020.9301976
State	Published - 23 Oct 2020
Event	2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020 - Yunlin, Taiwan Duration: 23 Oct 2020 → 25 Oct 2020

Publication series

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

Conference

Conference	2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020
Country/Territory	Taiwan
City	Yunlin
Period	23/10/20 → 25/10/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

GRU
autonomous vehicles (AVs)
deep learning
trajectory prediction

Access to Document

10.1109/ECICE50847.2020.9301976

Cite this

Hsu, P. Y., Lin Huang, M., & Chiang, H. H. (2020). Trajectory of Prediction of Immediate Surroundings for Autonomous Vehicles Using Hierarchical Deep Learning Model. In T.-H. Meen (Ed.), 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020 (pp. 263-266). Article 9301976 (2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECICE50847.2020.9301976

Hsu, Pei Yun ; Lin Huang, Mei ; Chiang, Hsin Han. / Trajectory of Prediction of Immediate Surroundings for Autonomous Vehicles Using Hierarchical Deep Learning Model. 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020. editor / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 263-266 (2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020).

@inproceedings{8b7842834db04633a24eb71b070331c2,

title = "Trajectory of Prediction of Immediate Surroundings for Autonomous Vehicles Using Hierarchical Deep Learning Model",

abstract = "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.",

keywords = "GRU, autonomous vehicles (AVs), deep learning, trajectory prediction",

author = "Hsu, \{Pei Yun\} and \{Lin Huang\}, Mei and Chiang, \{Hsin Han\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020 ; Conference date: 23-10-2020 Through 25-10-2020",

year = "2020",

month = oct,

day = "23",

doi = "10.1109/ECICE50847.2020.9301976",

language = "???core.languages.en\_GB???",

series = "2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "263--266",

editor = "Teen-Hang Meen",

booktitle = "2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020",

}

Hsu, PY, Lin Huang, M & Chiang, HH 2020, Trajectory of Prediction of Immediate Surroundings for Autonomous Vehicles Using Hierarchical Deep Learning Model. in T-H Meen (ed.), 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020., 9301976, 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020, Institute of Electrical and Electronics Engineers Inc., pp. 263-266, 2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020, Yunlin, Taiwan, 23/10/20. https://doi.org/10.1109/ECICE50847.2020.9301976

Trajectory of Prediction of Immediate Surroundings for Autonomous Vehicles Using Hierarchical Deep Learning Model. / Hsu, Pei Yun; Lin Huang, Mei; Chiang, Hsin Han.
2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020. ed. / Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2020. p. 263-266 9301976 (2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020).

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

TY - GEN

T1 - Trajectory of Prediction of Immediate Surroundings for Autonomous Vehicles Using Hierarchical Deep Learning Model

AU - Hsu, Pei Yun

AU - Lin Huang, Mei

AU - Chiang, Hsin Han

PY - 2020/10/23

Y1 - 2020/10/23

N2 - 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.

AB - 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.

KW - GRU

KW - autonomous vehicles (AVs)

KW - deep learning

KW - trajectory prediction

UR - https://www.scopus.com/pages/publications/85099581062

U2 - 10.1109/ECICE50847.2020.9301976

DO - 10.1109/ECICE50847.2020.9301976

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85099581062

T3 - 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020

SP - 263

EP - 266

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

A2 - Meen, Teen-Hang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020

Y2 - 23 October 2020 through 25 October 2020

ER -

Hsu PY, Lin Huang M, Chiang HH. Trajectory of Prediction of Immediate Surroundings for Autonomous Vehicles Using Hierarchical Deep Learning Model. In Meen TH, editor, 2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 263-266. 9301976. (2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020). doi: 10.1109/ECICE50847.2020.9301976

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this