ADMT: Advanced driver's movement tracking system using spatio-temporal interest points and maneuver anticipation using deep neural networks

Gite, S; Pradhan, B; Alamri, A; Kotecha, K

ADMT: Advanced driver's movement tracking system using spatio-temporal interest points and maneuver anticipation using deep neural networks

Gite, S Pradhan, B

Alamri, A Kotecha, K

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Access, 2021, 9, pp. 99312-99326
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Gite, S
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Alamri, A
dc.contributor.author	Kotecha, K
dc.date.accessioned	2022-02-17T04:39:45Z
dc.date.available	2022-02-17T04:39:45Z
dc.date.issued	2021-01-01
dc.identifier.citation	IEEE Access, 2021, 9, pp. 99312-99326
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/154639
dc.description.abstract	Assistive driving is a complex engineering problem and is influenced by several factors such as the sporadic nature of the quality of the environment, the response of the driver, and the standard of the roads on which the vehicle is being driven. The authors track the driver's anticipation based on his head movements using Spatio-Temporal Interest Point (STIP) extraction and enhance the anticipation of action accuracy well before using the RNN-LSTM framework. This research tackles a fundamental problem of lane change assistance by developing a novel model called Advanced Driver's Movement Tracking (ADMT). ADMT uses customized convolution-based deep learning networks by using Recurrent Convolutional Neural Network (RCNN). STIP with eye gaze extraction and RCNN performed in ADMT on brain4cars dataset for driver movement tracking. Its performance is compared with the traditional machine learning and deep learning models, namely Support Vector Machines (SVM), Hidden Markov Model (HMM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and provided an increment of almost 12% in the prediction accuracy and 44% in the anticipation time. Furthermore, ADMT systems outperformed all of the models in terms of both the accuracy of the system and the previously mentioned time of anticipation that is discussed at length in the paper. Thus it assists the driver with additional anticipation time to access the typical reaction time for better preparedness to respond to undesired future behavior. The driver is then assured of a safe and assisted driving experience with the proposed system.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/ACCESS.2021.3096032
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.title	ADMT: Advanced driver's movement tracking system using spatio-temporal interest points and maneuver anticipation using deep neural networks
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-02-17T04:39:43Z
pubs.publication-status	Published
pubs.volume	9

Abstract:

Assistive driving is a complex engineering problem and is influenced by several factors such as the sporadic nature of the quality of the environment, the response of the driver, and the standard of the roads on which the vehicle is being driven. The authors track the driver's anticipation based on his head movements using Spatio-Temporal Interest Point (STIP) extraction and enhance the anticipation of action accuracy well before using the RNN-LSTM framework. This research tackles a fundamental problem of lane change assistance by developing a novel model called Advanced Driver's Movement Tracking (ADMT). ADMT uses customized convolution-based deep learning networks by using Recurrent Convolutional Neural Network (RCNN). STIP with eye gaze extraction and RCNN performed in ADMT on brain4cars dataset for driver movement tracking. Its performance is compared with the traditional machine learning and deep learning models, namely Support Vector Machines (SVM), Hidden Markov Model (HMM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and provided an increment of almost 12% in the prediction accuracy and 44% in the anticipation time. Furthermore, ADMT systems outperformed all of the models in terms of both the accuracy of the system and the previously mentioned time of anticipation that is discussed at length in the paper. Thus it assists the driver with additional anticipation time to access the typical reaction time for better preparedness to respond to undesired future behavior. The driver is then assured of a safe and assisted driving experience with the proposed system.

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http://hdl.handle.net/10453/154639