[Abstract]:With the increase of vehicle ownership, people pay more and more attention to driving safety. As an important part of traffic system, driver's driving state directly determines the safety level of the whole traffic system. Driving fatigue is the most common among the many factors that affect driver's condition. Therefore, how to detect driver's driving state in real time and give out early warning in time when fatigue occurs can improve the level of traffic safety. It is of great significance to reduce the accident rate. In order to solve the above problems, a driving fatigue identification model based on ECG signal is constructed based on the existing research results. The main contents of this paper include the following: 1. This paper first introduces the background and significance of driving fatigue detection, analyzes the latest progress in the research object, research methods and research conclusions at home and abroad, and then puts forward the research content and technical route of this paper. 2. From the perspective of cognitive psychology, this paper expounds the mechanism of driving fatigue, and analyzes the inducing factors and fatigue characteristics of driving fatigue. The processing method and index extraction theory of ECG signal are introduced in detail. Through the analysis of heart rate variability and R-R interval, the extraction method of R-R interval is determined, and the method is verified with data. It lays a theoretical foundation for the further extraction of ECG indexes which can effectively represent the physiological state of drivers. 3. A long time simulation driving experiment with dual task paradigm is designed. The main task is to follow the vehicle and the second task is to respond to the brake signal by keystroke. After analyzing the ECG and behavior data collected in real time, it was found that with the occurrence of fatigue, the ECG and behavioral indexes of the subjects showed a certain trend, and after significant analysis, the early and late stages of the experiment. There are significant differences in the majority of ECG indexes, so we can preliminarily judge the ECG index which has good directivity to driving fatigue. 4. 4. The feasibility of using reaction time as the basis of fatigue grade classification is expounded. By analyzing the variation rule of simple reaction time in the whole experiment process, it is put forward that the fatigue grade is divided by reaction time. Firstly, the experiment process is divided into several periods, and the driving state of the first period is regarded as mild fatigue state. Through the significant analysis, the driving state of the remaining period is calibrated to distinguish the heavy and heavy fatigue states. In addition, through the correlation analysis with the reaction time, the ECG indexes which can effectively reflect the fatigue state are extracted, and the set of ECG indexes for fatigue identification is constructed. The fatigue identification model is constructed by using SVM theory. By continuously adjusting the composition of ECG index set and kernel function of fatigue identification, the recognition effect of the model is analyzed. It is found that when the time domain, frequency domain index and RBF kernel function are synthetically selected, The recognition effect of the model is optimal. Finally, the validity of the model is verified by experimental data.