Research On Driver Behavior Based On Channel State Information

Today,with the continuous development of networks,wireless local area networks(Wireless Local Area Network,WLAN)are increasingly used by people in broadband communications,wireless access and other services.Due to its advantages of wide coverage,high cost performance,and rapid deployment,researchers have also applied it to various other scientific research technology scenarios,the most widely used of which are human position detection and behavior perception.With the development of Internet of Vehicles technology,researchers began to use wireless local area networks to conduct driver perception and driver behavior research.The traditional driver behavior recognition research is mainly based on Received Signal Strength Indicator(RSSI),video,and infrared,but cameras,sensors,etc.have the disadvantages of expensive,inconvenient deployment,and low accuracy and instability of RSSI.In this paper,channel state information(Channel State Information,CSI)is used to conduct behavioral perception and action recognition research on the driver.Considering that the driving behavior of the driver is a time series process,this paper uses long-short-term memory neural based on traditional machine learning the network(Long-Short Term Memory,LSTM)combines various features to increase the robustness of the analysis system and improve the final classification accuracy.The main work of this article is as follows:(1)Analyze and summarize the traditional driver perception and detection technology,point out the shortcomings of the traditional technology,and analyze the advantages of the channel state information method;(2)Introduces the CSI physical basis,technical principles and acquired experimental platform,introduces commonly used machine learning algorithm classification models and introduces deep learning;(3)Designed and implemented a static driver discrimination experiment based on CSI,combined with machine learning to distinguish the principle of CSI perception of different drivers under the same car and specific experimental processing steps,the experimental results were analyzed and extended to different vehicles Verify scalability;(4)Designed and implemented a dynamic driver discrimination experiment based on CSI,that is,the research principle and specific experimental processing steps of different driving behaviors of the same driver,LSTM was introduced to strengthen the system robustness,and in many different scenarios and Several field experiments were carried out under various interference factors,and the influence of different factors and different characteristics on the system model was discussed.(5)Summarized the research achievements of different driver differentiation and driving behavior of the driver in this paper and prospected the future of the connected car world.