Research On Driving Behavior Recognition Based On Terminal Sensor

Driver’s driving behavior is one of the important factors affecting road traffic safety.With the emergence and development of terminal devices such as smart phones,the research on identification of driving behavior from the terminal device becomes more convenient.As devices that people carry with them,smart phones not only have a high penetration rate,but also contains many embedded sensors in them,which are very suitable for detecting and identifying driving behaviors.Therefore,on the basis of the existing research,this article further researches and explores the technology related to driving behavior recognition based on terminal sensors.In this regard,the major innovations of this paper are as following:First,a horizontal attitude fusion calibration algorithm based on terminal sensors is proposed.In practice,when terminal sensors are used to identify the driving behavior of a driver,most of them have strict requirements for the terminal’s placement posture and position in the vehicle to reflect the true information of the vehicle.In addition,different manufacturers use different sources of motion sensors,resulting in large differences in the accuracy performance of data collected by motion sensors,which is not conducive to use.Based on the data collected by the terminal motion sensors,the attitude differences between the built-in motion sensors of 5 different types of terminal equipment are analyzed,and a horizontal attitude fusion calibration algorithm based on the terminal sensors is designed to eliminate the differences between different types of sensors and improve precision of the data,the sensor’s attitude fusion and data calibration are realized,so that it can more accurately represent the movement status of the vehicle.Second,a driving behavior recognition algorithm based on terminal sensors is proposed.Based on data collected from terminal sensors,this paper analyzes the characteristics of various driving behavior,and proposes the rules for dividing normal and dangerous driving behaviors.This paper also optimizes and applies the proposed horizontal attitude fusion calibration algorithm,and uses a machine learning algorithm,XGBoost,which performs well on sparse data,to realize driving behavior recognition based on terminal sensors.Experimental results show that the algorithm proposed in this paper successfully predicts and recognizes many types of driving behaviors with high accuracy and recall performance,and thus has good adaptability and practical application prospects.Finally,this paper optimizes the overall algorithm framework to verify the robustness of the two algorithms mentioned above.A driving behavior recognition algorithm with streaming input was designed,the above two algorithms were optimized and integrated,and a joint test of the overall platform was performed.The test results show that the algorithm successfully achieves the reachable scheme of driving behavior streaming recognition based on terminal sensor data,and verifies that the horizontal attitude fusion calibration algorithm and driving behavior recognition algorithm based on terminal sensor proposed in this paper can not only achieve satisfying accuracy,but also have good universality,adaptability and broad practical application prospects.