Design of Driver Stress Prediction Model with CNN-LSTM: Exploration of Feature Space using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{yang:2024:IJCNN,
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author = "Tingting Yang and Chenhao Xue and Jun Chen2",
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title = "Design of Driver Stress Prediction Model with
{CNN-LSTM:} Exploration of Feature Space using Genetic
Programming",
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booktitle = "2024 International Joint Conference on Neural Networks
(IJCNN)",
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year = "2024",
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editor = "Chrisina Jayne",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Accuracy,
Predictive models, Stability analysis, Physiology,
Space exploration, Convolutional neural networks,
Driver Stress Prediction, CNN, LSTM, Features
Exploration, Representation Learning",
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isbn13 = "979-8-3503-5932-9",
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DOI = "doi:10.1109/IJCNN60899.2024.10650395",
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abstract = "Road traffic accidents, primarily caused by
driver-related issues such as stress, result in
numerous fatalities and injuries. Effective prediction
of driver stress within the driving phase is paramount
for real-time accident intervention, while it requires
prediction accuracy, stability, and enough predictive
lead time. Physiological data contains rich information
related to driver stress levels, which can be captured
by machine learning models. However, those models are
mainly developed for and perform well in static stress
detection tasks, not addressing practical requirements
of predictive lead time and performance stability. This
study introduces a novel approach, the GP-CNN-LSTM
model, Which employs a Convolutional Neural Network
(CNN) with a Long Short-Term Memory (LSTM) network and
Genetic Programming (GP), leverages GP to explore the
feature space upon physiological sensor signals, and
relies on CNN-LSTM to predict driver stress.
Experiments show that this model achieves high accuracy
for a 60-second forward stress prediction, with more
stability compared to Fractional Fourier
Transform-based benchmark models. We found explainable
effective signals and features described by math
functions via genetic programming that helped in
improving the accuracy and stability of driver stress
prediction.",
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notes = "also known as \cite{10650395}
WCCI 2024",
- }
Genetic Programming entries for
Tingting Yang
Chenhao Xue
Jun Chen2
Citations