HEAD POSE AND EYE GAZE FEATURE RECOGNITION BASED PREDICTION OF DRIVERS' MANEUVERS

Fei Qi; Yue Ma; Ao Li; Qi Yan

doi:10.1049/icp.2021.0179

HEAD POSE AND EYE GAZE FEATURE RECOGNITION BASED PREDICTION OF DRIVERS' MANEUVERS

Fei Qi, Yue Ma^*, Ao Li, Qi Yan

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

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

Abstract

The interactions between human, machine systems and environment have great significance for making the interactions and systems autonomous and intelligent. To improve the interaction between drivers and vehicles, a driver's operation prediction framework based on learning method and fusion of multi-sensors information was proposed to get the prediction of potential maneuver. The conclusion gives that the proposed framework can predict driver's maneuvers with a high precision of 90%.

Original language	English
Title of host publication	IET Conference Proceedings
Publisher	Institution of Engineering and Technology
Pages	369-375
Number of pages	7
Volume	2020
Edition	3
ISBN (Electronic)	9781839534195
DOIs	https://doi.org/10.1049/icp.2021.0179
Publication status	Published - 2020
Event	2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020 - Virtual, Online Duration: 18 Sept 2020 → 21 Sept 2020

Conference

Conference	2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020
City	Virtual, Online
Period	18/09/20 → 21/09/20

Keywords

EYE GAZE
FEATURE-BASED
HEAD POSE
PREDICTION

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10.1049/icp.2021.0179

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title = "HEAD POSE AND EYE GAZE FEATURE RECOGNITION BASED PREDICTION OF DRIVERS' MANEUVERS",

abstract = "The interactions between human, machine systems and environment have great significance for making the interactions and systems autonomous and intelligent. To improve the interaction between drivers and vehicles, a driver's operation prediction framework based on learning method and fusion of multi-sensors information was proposed to get the prediction of potential maneuver. The conclusion gives that the proposed framework can predict driver's maneuvers with a high precision of 90%.",

keywords = "EYE GAZE, FEATURE-BASED, HEAD POSE, PREDICTION",

author = "Fei Qi and Yue Ma and Ao Li and Qi Yan",

note = "Publisher Copyright: {\textcopyright} 2020 The Institution of Engineering and Technology.; 2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020 ; Conference date: 18-09-2020 Through 21-09-2020",

year = "2020",

doi = "10.1049/icp.2021.0179",

language = "English",

volume = "2020",

pages = "369--375",

booktitle = "IET Conference Proceedings",

publisher = "Institution of Engineering and Technology",

address = "United Kingdom",

edition = "3",

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T1 - HEAD POSE AND EYE GAZE FEATURE RECOGNITION BASED PREDICTION OF DRIVERS' MANEUVERS

AU - Qi, Fei

AU - Ma, Yue

AU - Li, Ao

AU - Yan, Qi

PY - 2020

Y1 - 2020

N2 - The interactions between human, machine systems and environment have great significance for making the interactions and systems autonomous and intelligent. To improve the interaction between drivers and vehicles, a driver's operation prediction framework based on learning method and fusion of multi-sensors information was proposed to get the prediction of potential maneuver. The conclusion gives that the proposed framework can predict driver's maneuvers with a high precision of 90%.

AB - The interactions between human, machine systems and environment have great significance for making the interactions and systems autonomous and intelligent. To improve the interaction between drivers and vehicles, a driver's operation prediction framework based on learning method and fusion of multi-sensors information was proposed to get the prediction of potential maneuver. The conclusion gives that the proposed framework can predict driver's maneuvers with a high precision of 90%.

KW - EYE GAZE

KW - FEATURE-BASED

KW - HEAD POSE

KW - PREDICTION

UR - http://www.scopus.com/inward/record.url?scp=85174645036&partnerID=8YFLogxK

U2 - 10.1049/icp.2021.0179

DO - 10.1049/icp.2021.0179

M3 - Conference contribution

AN - SCOPUS:85174645036

VL - 2020

SP - 369

EP - 375

BT - IET Conference Proceedings

PB - Institution of Engineering and Technology

T2 - 2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020

Y2 - 18 September 2020 through 21 September 2020

ER -

HEAD POSE AND EYE GAZE FEATURE RECOGNITION BASED PREDICTION OF DRIVERS' MANEUVERS

Abstract

Conference

Keywords

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