RT Journal Article
A1 Yangliu Dou
A1 Yihao Fang
A1 Chuan Hu
A1 Rong Zheng
A1 Fengjun Yan

PB iet
T1 Gated branch neural network for mandatory lane changing suggestion at the on-ramps of highway
JN IET Intelligent Transport Systems
VO 13
IS 1
SP 48
OP 54
AB 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.
K1 adaptive moment estimation optimiser
K1 correlation analysis
K1 on-ramps-of-highway
K1 nonmerge event
K1 NGSIM
K1 U.S. Highway 101
K1 nonmerge accuracy
K1 gated branch neural network
K1 GBNN algorithm
K1 U.S. Interstate 80
K1 ADAS
K1 Python
K1 deep learning algorithm
K1 mandatory lane changing behaviour modelling
K1 Pearson correlation method
K1 receiver operating characteristic score
K1 MLC behaviour modelling
K1 mandatory lane changing suggestion
K1 Federal Highway Administration Next Generation Simulation
K1 real-world dataset
K1 advanced driver assistance systems
K1 merge accuracy
K1 TensorFlow framework
K1 MLC suggestion system
K1 principal component analysis
K1 scaled exponential linear units activation function
K1 merge event
DO https://doi.org/10.1049/iet-its.2018.5093
UL https://digital-library.theiet.org/;jsessionid=40l041eq8p4k3.x-iet-live-01content/journals/10.1049/iet-its.2018.5093
LA English
SN 1751-956X
YR 2019
OL EN