Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reli...Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reliability under severe traffic scenes. This paper proposes a new ADBI method based on direction and position offsets,where a two-factor identification strategy is proposed to improve the accuracy and reliability of ADBI. Self-adaptive edge detection based on Sobel operator is used to extract edge information of lanes. In order to enhance the efficiency and reliability of lane detection,an improved lane detection algorithm is proposed,where a Hough transform based on local search scope is employed to quickly detect the lane,and a validation scheme based on priori information is proposed to further verify the detected lane. Experimental results under various complex road conditions demonstrate the validity of the proposed ADBI.展开更多
Aiming at drivers’dangerous driving behavior monitoring and health monitoring,this paper designs an intelligent steering wheel that can monitor dan-gerous driving behavior and a steering wheel sleeve that can monitor...Aiming at drivers’dangerous driving behavior monitoring and health monitoring,this paper designs an intelligent steering wheel that can monitor dan-gerous driving behavior and a steering wheel sleeve that can monitor physical health.The MTCNN model is primarily used to obtain a driver’s face image in real time.The PFLD algorithm was used to obtain the facial model positioning feature points,and the degree of driver fatigue was determined by combining the relevant parameters.The fatigue algorithm proposed in this paper can improve the effectiveness and accuracy of monitoring.Then,according to the LSTM network model,11 groups of key point information of the human body are obtained,and the human motion track is identified and then combined with the facial information to complete the judgment of driving behavior such as drinking water,smoking and walking.Through the PPG and ECG fusion algorithm based on LSTM,the reliability of the system to collect vital signs such as body temperature,blood pressure,heart rate and blood oxygen of the driver is improved.It was determined that the system could monitor a driver’s driving behavior in real time and consider its health management.展开更多
基金Supported by the National Natural Science Foundation of China(No.61304205,61502240)Natural Science Foundation of Jiangsu Province(BK20141002)+1 种基金Innovation and Entrepreneurship Training Project of College Students(No.201710300051,201710300050)Foundation for Excellent Undergraduate Dissertation(Design) of Naning University of Information Science & Technology
文摘Abnormal driving behavior identification( ADBI) has become a research hotspot because of its significance in driver assistance systems. However,current methods still have some limitations in terms of accuracy and reliability under severe traffic scenes. This paper proposes a new ADBI method based on direction and position offsets,where a two-factor identification strategy is proposed to improve the accuracy and reliability of ADBI. Self-adaptive edge detection based on Sobel operator is used to extract edge information of lanes. In order to enhance the efficiency and reliability of lane detection,an improved lane detection algorithm is proposed,where a Hough transform based on local search scope is employed to quickly detect the lane,and a validation scheme based on priori information is proposed to further verify the detected lane. Experimental results under various complex road conditions demonstrate the validity of the proposed ADBI.
文摘Aiming at drivers’dangerous driving behavior monitoring and health monitoring,this paper designs an intelligent steering wheel that can monitor dan-gerous driving behavior and a steering wheel sleeve that can monitor physical health.The MTCNN model is primarily used to obtain a driver’s face image in real time.The PFLD algorithm was used to obtain the facial model positioning feature points,and the degree of driver fatigue was determined by combining the relevant parameters.The fatigue algorithm proposed in this paper can improve the effectiveness and accuracy of monitoring.Then,according to the LSTM network model,11 groups of key point information of the human body are obtained,and the human motion track is identified and then combined with the facial information to complete the judgment of driving behavior such as drinking water,smoking and walking.Through the PPG and ECG fusion algorithm based on LSTM,the reliability of the system to collect vital signs such as body temperature,blood pressure,heart rate and blood oxygen of the driver is improved.It was determined that the system could monitor a driver’s driving behavior in real time and consider its health management.
