Author(s)

Xu Yan^*, FangYong Xu, Hao Ma, AoXiang Wang, HongZhen Liang, ZiHao Wang, Jian Yao

Affiliation(s)

School of Mechanical and Electronic Engineering, Shandong Jianzhu University, Jinan 250101, Shandong, China.

Corresponding Author

Xu Yan

ABSTRACT

This study focuses on utilizing WIFI signal perception technology to monitor driving behaviors, aiming to provide an innovative and efficient solution for intelligent transportation systems. By delving into the principles, characteristics, and operational steps of WIFI perception technology, and integrating deep learning algorithms to construct a model for identifying dangerous driving behaviors, extensive experimental validations were conducted. The research demonstrates that this method can accurately identify obvious behaviors such as sudden acceleration and hard braking, as well as subtle behaviors like distracted and fatigued driving under certain conditions. This approach not only achieves non-invasive, all-weather, and privacy-friendly driving behavior recognition but also provides real-time warning support in complex environments, significantly reducing traffic accident rates caused by dangerous driving. Moreover, this study pioneers the integration of the fine-grained characteristics of WIFI signals with spatiotemporal deep networks, overcoming the limitations of traditional monitoring technologies and injecting new vitality into the field of intelligent transportation. It also offers significant references for further optimizing driving behavior monitoring.

KEYWORDS

WiFi signal perception; Driver behavior; Intelligent transportation; Deep learning

CITE THIS PAPER

Xu Yan, FangYong Xu, Hao Ma, AoXiang Wang, HongZhen Liang, ZiHao Wang, Jian Yao. Driving behavior utilizing WiFi signal perception. World Journal of Information Technology. 2025, 3(2): 37-44. DOI: https://doi.org/10.61784/wjit3032.

REFERENCES

[1] Li Jiankan, Li Zewei, Hua Wenwen, et al. Statistical Analysis of Urban Road Traffic Accidents. Science and Technology Innovation and Application, 2021, 11(21): 74-76.

[2] Zheng Xiaokun. Research on the Fusion Technology of WiFi Communication Preamble and Radar Sensing Signals. Beijing University of Posts and Telecommunications, 2021.

[3] Xu Xiaoming. Research on Driving Behavior Recognition Technology Based on FMCW Radar. Nanjing University of Science and Technology, 2020.

[4] Zhou Feiyan, Jin Linpeng, Dong Jun. Review of Convolutional Neural Network Research. Journal of Computer, 2017, 40(06): 1229-1251.

[5] Pan Xingda. Research on Driver Behavior Based on Channel State Information. Zhejiang University of Technology, 2020.

[6] Wu Zhefu, Gong Zhengang, Gong Shufeng, et al. Driver Action Detection Based on Channel State Information. Journal of Transducer Technology, 2020, 33(12): 1808-1813.

[7] Yang Lin, Zhang Lei, Liu Bailong, et al. Driver Recognition Model Based on Driving Context Awareness. Computer Engineering and Science, 2025, 47(03): 548-560.

[8] Yang Haifei, Hua Yixuan, Su Haiqi, et al. Characteristics and Optimization Strategies of Driver Perception Response Points on Traffic Flow Hysteresis. Journal of Transportation Engineering and Information, 2025, 1-16.

[9] Jin Lianghai, Liu Hao, Wu Bangjie, et al. Situation Awareness and Behavioral Response Model of Crane Drivers Based on DEMATEL-AISM. China Safety Science Journal, 2024, 34(09): 1-8.

[10] Wang Teng, Bi Jingxue, Chen Guoliang, et al. Review on Acquisition and Application of Wireless Sensing Data Based on Channel State Information. Navigation Positioning and Timing, 2024, 11(01): 10-29.

[11] Li Shaofan, Gao Shangbing, Zhang Yingying. Pose-Guided Instance Perception Learning for Driver Distraction Behavior Recognition. Journal of Image and Graphics, 2023, 28(11): 3550-3561.

[12] Li Xiaojie. Research on Signal Perception Modeling and Simulation of Virtual Traffic Scenarios. Chang'an University, 2022, 3. DOI: 10.26976/d.cnki.gchau.2021.002092.

[13] Angomas E, Blinka D M, Kelly E, et al. “It’s Usually Not Dementia That’s the Focus”: Barriers and Facilitators of Dementia Care in Primary Care. Journal of General Internal Medicine, 2025, (prepublish): 1-11.

[14] Pitt M T, Macpherson A, Fridman L, et al. Risky driving behaviors at school drop-off across Canadian municipalities: Findings from the Child Active Transportation Safety and the Environment (CHASE) study. Traffic injury prevention, 2025, 1-5.

[15] Alenzi E, Hassan A S, Puan C O. Seasonal Influences on Driver Behaviour: A Review of Car-Following Dynamics in Hot and Cold Climates. International Journal on Transport Development and Integration, 2025, 9(1): 117-130.

[16] Barbosa W M, Miranda O D V J, Oliveira D K L. Identification of antecedents of risky driving behavior of food delivery riders: An analysis during the COVID -19 pandemic in Brazil. Case Studies on Transport Policy, 2025, 20101403.

[17] Sekadakis M, Trosterer S, Moertl P, et al. Identifying driving profiles after take over request in automated vehicles at SAE levels 2 and 3. Transportation Research Part F: Psychology and Behaviour, 2025, 111250-111263.