Neural Network-Based Safety Analysis of Driving Behavior in Desert Areas
In order to explore the influence of sand accumulation road surface on traffic safety under wind and sand environment. This paper relies on the desert environment real-vehicle test to study the correlation between drivers’ EEG data and driving behavior under different road alignments and...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10942355/ |
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| author | Fang Wang Siping Huang Weiguo Sun Shixiao Liu Huan Zhang |
| author_facet | Fang Wang Siping Huang Weiguo Sun Shixiao Liu Huan Zhang |
| author_sort | Fang Wang |
| collection | DOAJ |
| description | In order to explore the influence of sand accumulation road surface on traffic safety under wind and sand environment. This paper relies on the desert environment real-vehicle test to study the correlation between drivers’ EEG data and driving behavior under different road alignments and different sand thicknesses, constructs behavioral spectra, uses K-means to classify dangerous driving behaviors into four grades, and applies MATLAB software to construct the BP and RBF neural network recognition model. The study shows that 1) driver’s EEG <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> wave PSD integral values are positively correlated with the thickness of sand on the road and the complexity of the road alignment; 2) drivers tend to change the speed to reduce the driving risk when there is no sand accumulation to 0-2mm and the flat straight road to the flat curved road, and drivers tend to change the driving direction to avoid the dangerous driving environment and reduce the driving risk when there is a combination of road sand thickness of 4-6mm and curved and sloped road; 3) spectrum of risky driving behavior characteristics is mainly distributed in the (0, 0.2] interval, and the risk of serious driving increases significantly in the <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>/<inline-formula> <tex-math notation="LaTeX">$\beta =0.81$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>/<inline-formula> <tex-math notation="LaTeX">$\beta =0.89$ </tex-math></inline-formula> states, which account for 2.16 times and 5.28 times more than the <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>/<inline-formula> <tex-math notation="LaTeX">$\beta =1.01$ </tex-math></inline-formula> state, respectively. The results of the study can provide a basis for driving safety and security in desert areas, and reduce the rate of traffic accidents in desert areas to a certain extent. |
| format | Article |
| id | doaj-art-dfb31072c9094de3856f12fa35065cbe |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-dfb31072c9094de3856f12fa35065cbe2025-08-20T03:07:05ZengIEEEIEEE Access2169-35362025-01-0113556575566910.1109/ACCESS.2025.355475810942355Neural Network-Based Safety Analysis of Driving Behavior in Desert AreasFang Wang0https://orcid.org/0000-0002-1816-069XSiping Huang1https://orcid.org/0009-0002-3695-4255Weiguo Sun2Shixiao Liu3Huan Zhang4https://orcid.org/0009-0001-2060-5459College of Civil and Hydraulic Engineering, Ningxia University, Yinchuan, Ningxia, ChinaCollege of Civil and Hydraulic Engineering, Ningxia University, Yinchuan, Ningxia, ChinaNingxia Road and Bridge Construction Company Ltd., Yinchuan, Ningxia, ChinaCollege of Civil and Hydraulic Engineering, Ningxia University, Yinchuan, Ningxia, ChinaCollege of Civil and Hydraulic Engineering, Ningxia University, Yinchuan, Ningxia, ChinaIn order to explore the influence of sand accumulation road surface on traffic safety under wind and sand environment. This paper relies on the desert environment real-vehicle test to study the correlation between drivers’ EEG data and driving behavior under different road alignments and different sand thicknesses, constructs behavioral spectra, uses K-means to classify dangerous driving behaviors into four grades, and applies MATLAB software to construct the BP and RBF neural network recognition model. The study shows that 1) driver’s EEG <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> wave PSD integral values are positively correlated with the thickness of sand on the road and the complexity of the road alignment; 2) drivers tend to change the speed to reduce the driving risk when there is no sand accumulation to 0-2mm and the flat straight road to the flat curved road, and drivers tend to change the driving direction to avoid the dangerous driving environment and reduce the driving risk when there is a combination of road sand thickness of 4-6mm and curved and sloped road; 3) spectrum of risky driving behavior characteristics is mainly distributed in the (0, 0.2] interval, and the risk of serious driving increases significantly in the <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>/<inline-formula> <tex-math notation="LaTeX">$\beta =0.81$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>/<inline-formula> <tex-math notation="LaTeX">$\beta =0.89$ </tex-math></inline-formula> states, which account for 2.16 times and 5.28 times more than the <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>/<inline-formula> <tex-math notation="LaTeX">$\beta =1.01$ </tex-math></inline-formula> state, respectively. The results of the study can provide a basis for driving safety and security in desert areas, and reduce the rate of traffic accidents in desert areas to a certain extent.https://ieeexplore.ieee.org/document/10942355/Driving safetybrain wavesneural networksroad surface sand thicknessroad alignmentbehavioral spectrum |
| spellingShingle | Fang Wang Siping Huang Weiguo Sun Shixiao Liu Huan Zhang Neural Network-Based Safety Analysis of Driving Behavior in Desert Areas IEEE Access Driving safety brain waves neural networks road surface sand thickness road alignment behavioral spectrum |
| title | Neural Network-Based Safety Analysis of Driving Behavior in Desert Areas |
| title_full | Neural Network-Based Safety Analysis of Driving Behavior in Desert Areas |
| title_fullStr | Neural Network-Based Safety Analysis of Driving Behavior in Desert Areas |
| title_full_unstemmed | Neural Network-Based Safety Analysis of Driving Behavior in Desert Areas |
| title_short | Neural Network-Based Safety Analysis of Driving Behavior in Desert Areas |
| title_sort | neural network based safety analysis of driving behavior in desert areas |
| topic | Driving safety brain waves neural networks road surface sand thickness road alignment behavioral spectrum |
| url | https://ieeexplore.ieee.org/document/10942355/ |
| work_keys_str_mv | AT fangwang neuralnetworkbasedsafetyanalysisofdrivingbehaviorindesertareas AT sipinghuang neuralnetworkbasedsafetyanalysisofdrivingbehaviorindesertareas AT weiguosun neuralnetworkbasedsafetyanalysisofdrivingbehaviorindesertareas AT shixiaoliu neuralnetworkbasedsafetyanalysisofdrivingbehaviorindesertareas AT huanzhang neuralnetworkbasedsafetyanalysisofdrivingbehaviorindesertareas |