NDLI: Gated branch neural network for mandatory lane changing suggestion at the on-ramps of highway

Content Provider	IET Digital Library
Author	Dou, Yangliu Fang, Yihao Hu, Chuan Zheng, Rong Yan, Fengjun
Abstract	A gated branch neural network (GBNN) is proposed for modelling mandatory lane changing (MLC) behaviour at the on-ramps of highways. It provides a core algorithm for an MLC suggestion system for advanced driver assistance systems (ADAS), where the main challenge is the trade-off between computational speed and prediction accuracy for both non-merge and merge events. The GBNN algorithm employs a gated branch based on correlation analysis, scaled exponential linear units activation function, and adaptive moment estimation optimiser. The algorithm has been evaluated using the real-world dataset of U.S. Highway 101 and Interstate 80 from Federal Highway Administration's Next Generation Simulation (NGSIM). Input features are extracted from NGSIM and pre-processed by standardisation and principal component analysis. TensorFlow framework and Python are used as the development platform. Results show that the proposed GBNN algorithm with the Pearson correlation method has values of 97.7%, 96.3%, and 0.990 for non-merge accuracy, merge accuracy, and receiver operating characteristic score, respectively. It outperforms other traditional binary classifiers for MLC applications, and is more light-weight than a convolutional neural network (AlexNet) of deep learning algorithm. Owing to its compact architecture, the GBNN provides high accuracy and efficiency, demonstrating promising usage as an MLC suggestion system in ADAS.
Starting Page	48
Ending Page	54
Page Count	7
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 1, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5093
Journal	IET Intelligent Transport Systems
Publisher Date	2018-07-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Moment Estimation Optimiser ADAS Advanced Driver Assistance System Behavioural Sciences Computing Correlation Analysis Deep Learning Algorithm Driver Information System Federal Highway Administration Next Generation Simulation Gated Branch Neural Network GBNN Algorithm Knowledge Engineering Technique Learning in AI Mandatory Lane Changing Behaviour Modelling Mandatory Lane Changing Suggestion Merge Accuracy Merge Event MLC Behaviour Modelling MLC Suggestion System Neural Computing Technique Neural Nets NGSIM Nonmerge Accuracy Nonmerge Event On-ramps-of-highway Optimisation Optimisation Technique Pearson Correlation Method Principal Component Analysis Python Real-world Dataset Receiver Operating Characteristic Score Scaled Exponential Linear Units Activation Function Social And Behavioural Sciences Computing Statistics TensorFlow Framework Traffic Engineering Computing U.S. Highway 101 U.S. Interstate 80
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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