Pedestrian Behavior Prediction Method for Intelligent Vehicles Based on Convolutional Neural Network

Hongbo Gao; Xi He; Liuchang Wang; Fei Zhang; Kaiquan Cai; Xiaozhao Fang

doi:10.1109/ICUS55513.2022.9987009

Pedestrian Behavior Prediction Method for Intelligent Vehicles Based on Convolutional Neural Network

Hongbo Gao
, Xi He
, Liuchang Wang
, Fei Zhang^*
, Kaiquan Cai
, Xiaozhao Fang

^*Corresponding author for this work

School of Electronic and Information Engineering

University of Science and Technology of China
School of Mathematical Sciences, Anhui University
Guangdong University of Technology

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

4 Scopus citations

Abstract

Convolutional neural network has excellent representation learning ability, which makes it unique in the field of behavior prediction. This paper presents a prediction method of pedestrian behavior around intelligent vehicles, which makes use of the advantages of convolutional neural network, combines pedestrian intention and road environment information to predict pedestrian behavior around intelligent vehicle, and optimizes pedestrian avoidance module in automatic driving system. The experimental results show that the area of the closed graph composed of the predicted trajectory and the actual trajectory is 0.1269 m2. The method proposed in this paper can effectively predict the pedestrian behavior trajectory, ensure the safety of drivers and pedestrians to the maximum extent, and provide a new solution for intelligent vehicles and intelligent driving path planning.

Original language	English
Title of host publication	Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022
Editors	Rong Song
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	787-792
Number of pages	6
ISBN (Electronic)	9781665484565
DOIs	https://doi.org/10.1109/ICUS55513.2022.9987009
State	Published - 2022
Event	2022 IEEE International Conference on Unmanned Systems, ICUS 2022 - Guangzhou, China Duration: 28 Oct 2022 → 30 Oct 2022

Publication series

Name	Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022

Conference

Conference	2022 IEEE International Conference on Unmanned Systems, ICUS 2022
Country/Territory	China
City	Guangzhou
Period	28/10/22 → 30/10/22

Keywords

Convolution Neural Network
Deep Learning
Intelligent Drive
Intelligent Vehicles
Pedestrian Trajectory Prediction

Access to Document

10.1109/ICUS55513.2022.9987009

Cite this

Gao, H., He, X., Wang, L., Zhang, F., Cai, K., & Fang, X. (2022). Pedestrian Behavior Prediction Method for Intelligent Vehicles Based on Convolutional Neural Network. In R. Song (Ed.), Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022 (pp. 787-792). (Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICUS55513.2022.9987009

Gao, Hongbo ; He, Xi ; Wang, Liuchang et al. / Pedestrian Behavior Prediction Method for Intelligent Vehicles Based on Convolutional Neural Network. Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022. editor / Rong Song. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 787-792 (Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022).

@inproceedings{12d6a7ef15534cabbf3e7b30ba92ab8f,

title = "Pedestrian Behavior Prediction Method for Intelligent Vehicles Based on Convolutional Neural Network",

abstract = "Convolutional neural network has excellent representation learning ability, which makes it unique in the field of behavior prediction. This paper presents a prediction method of pedestrian behavior around intelligent vehicles, which makes use of the advantages of convolutional neural network, combines pedestrian intention and road environment information to predict pedestrian behavior around intelligent vehicle, and optimizes pedestrian avoidance module in automatic driving system. The experimental results show that the area of the closed graph composed of the predicted trajectory and the actual trajectory is 0.1269 m2. The method proposed in this paper can effectively predict the pedestrian behavior trajectory, ensure the safety of drivers and pedestrians to the maximum extent, and provide a new solution for intelligent vehicles and intelligent driving path planning.",

keywords = "Convolution Neural Network, Deep Learning, Intelligent Drive, Intelligent Vehicles, Pedestrian Trajectory Prediction",

author = "Hongbo Gao and Xi He and Liuchang Wang and Fei Zhang and Kaiquan Cai and Xiaozhao Fang",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Unmanned Systems, ICUS 2022 ; Conference date: 28-10-2022 Through 30-10-2022",

year = "2022",

doi = "10.1109/ICUS55513.2022.9987009",

language = "英语",

series = "Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022",

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

pages = "787--792",

editor = "Rong Song",

booktitle = "Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022",

address = "美国",

}

Gao, H, He, X, Wang, L, Zhang, F, Cai, K & Fang, X 2022, Pedestrian Behavior Prediction Method for Intelligent Vehicles Based on Convolutional Neural Network. in R Song (ed.), Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022. Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022, Institute of Electrical and Electronics Engineers Inc., pp. 787-792, 2022 IEEE International Conference on Unmanned Systems, ICUS 2022, Guangzhou, China, 28/10/22. https://doi.org/10.1109/ICUS55513.2022.9987009

Pedestrian Behavior Prediction Method for Intelligent Vehicles Based on Convolutional Neural Network. / Gao, Hongbo; He, Xi; Wang, Liuchang et al.
Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022. ed. / Rong Song. Institute of Electrical and Electronics Engineers Inc., 2022. p. 787-792 (Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022).

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

TY - GEN

T1 - Pedestrian Behavior Prediction Method for Intelligent Vehicles Based on Convolutional Neural Network

AU - Gao, Hongbo

AU - He, Xi

AU - Wang, Liuchang

AU - Zhang, Fei

AU - Cai, Kaiquan

AU - Fang, Xiaozhao

PY - 2022

Y1 - 2022

N2 - Convolutional neural network has excellent representation learning ability, which makes it unique in the field of behavior prediction. This paper presents a prediction method of pedestrian behavior around intelligent vehicles, which makes use of the advantages of convolutional neural network, combines pedestrian intention and road environment information to predict pedestrian behavior around intelligent vehicle, and optimizes pedestrian avoidance module in automatic driving system. The experimental results show that the area of the closed graph composed of the predicted trajectory and the actual trajectory is 0.1269 m2. The method proposed in this paper can effectively predict the pedestrian behavior trajectory, ensure the safety of drivers and pedestrians to the maximum extent, and provide a new solution for intelligent vehicles and intelligent driving path planning.

AB - Convolutional neural network has excellent representation learning ability, which makes it unique in the field of behavior prediction. This paper presents a prediction method of pedestrian behavior around intelligent vehicles, which makes use of the advantages of convolutional neural network, combines pedestrian intention and road environment information to predict pedestrian behavior around intelligent vehicle, and optimizes pedestrian avoidance module in automatic driving system. The experimental results show that the area of the closed graph composed of the predicted trajectory and the actual trajectory is 0.1269 m2. The method proposed in this paper can effectively predict the pedestrian behavior trajectory, ensure the safety of drivers and pedestrians to the maximum extent, and provide a new solution for intelligent vehicles and intelligent driving path planning.

KW - Convolution Neural Network

KW - Deep Learning

KW - Intelligent Drive

KW - Intelligent Vehicles

KW - Pedestrian Trajectory Prediction

UR - https://www.scopus.com/pages/publications/85146490252

U2 - 10.1109/ICUS55513.2022.9987009

DO - 10.1109/ICUS55513.2022.9987009

M3 - 会议稿件

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T3 - Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022

SP - 787

EP - 792

BT - Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022

A2 - Song, Rong

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Unmanned Systems, ICUS 2022

Y2 - 28 October 2022 through 30 October 2022

ER -

Gao H, He X, Wang L, Zhang F, Cai K, Fang X. Pedestrian Behavior Prediction Method for Intelligent Vehicles Based on Convolutional Neural Network. In Song R, editor, Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 787-792. (Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022). doi: 10.1109/ICUS55513.2022.9987009

Pedestrian Behavior Prediction Method for Intelligent Vehicles Based on Convolutional Neural Network

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