Theories And Methods On Driving Risk Status Identification

Transportation safety problems have become more and more obvious since urban motorized level has improved. Being a core factor of road traffic system, drivers' driving status plays a rather important role in safety level of road traffic system. Thus it becomes a key issue of road traffic safety to study on driving behavior status. Most of researches at the present are focused on comparing the static differences between accident and non-accident groups on drivers' physiological indexes and mental ones. And they have discussed the disturbance of some given factor, as well as some correlations among drivers' physiology, psychology, characteristics and behaviors. The shortage is those cannot analyze the relationship among accidents rate, different status level and different time on the basis of time sequence, which can provide some evidences for system safety control and make real-time system safety levels. To solve the above problems, the dissertation constructs a series of theories and methods to identify driving behavior risk status and study on some related modules. The purposes are to realize process identify of driving behaviors and supply driving risk levels dynamically, which can control driving process and decrease or prevent traffic accidents on roads. The summary is as follow:The formation mechanism of driving behavior is discussed combining with a chain of information acquisition, information treatment and handle output, on the basis of cognitive psychology. It gives out an objective definition of driving ability indexes set and discusses how a risk can generated according to analyze the coupling matching ability between road traffic system tasks demand ability and actual driving ability. The formation mechanism is resolute as three basic modes which are mutation, synchronization and asynchronous gradual change. It also proposes a module of traffic accidents formation mechanism, which explain the production principles of risk and accidents as well as how they convert. Mathematical derivation has proved that driving status level will gradually decreased and system risk will increased along with the driving time passes. The result is consistent to actual situation. The paper puts forward to a quantity method to classify three types driving risk status. (1) Risk status classify based on economy: As risk analysis theory is introduced into the research on driving hazards, it gives out a concept of driving risk on the basis of risk essence and determines the composite relation between accidents probability and accidents lost in driving risk, and the method to find them out. On ALARP principle, driving risk statuses are divided into neglected class, tolerable class and intolerable class, after which safety margin cost profit for breakpoints for different classes with regards to information fusion algorithm. (2) Risk status classify based on status indexes variability: Exponents smoothing and average smoothing must be pretreated towards indexes data of driving status to eliminate random errors disturbance and show their essential trend out. Then calculate quadratic differential value of every index data, referring to which the breakpoints of every class can be determined. (3) Risk status classify based on similarity of behavior state: first to make time period averaged for driving status index test data, then set driving risk status class number k, classify optimization of driving status can be realizes if sequential cluster method is adopted.A way of quantization disjunction has been constructed here to find out driving risk discrimination factor. Test method and calculating formulas of ten indexes about driving status first are described here, which are dynamic visual field, dynamic vision, dark adaptation, hearing, masking hearing, short-time memory, thinking judgment ability, focus, reaction time and handling ability. Then we did an 12h' continuous simulated driving test on grouped drivers by gender, age and driving mileage, collecting driving status factors values of one group every 15min.We have to classify driving status indexes data before analyze the collected data by single factor method. The result shows an obvious difference (p≤0.05) among reaction time, focus and judgment ability on different classes. Thus they can be used as main factors of driving risk status identification.A probability model of driver attention state under high-load driving task is porposed. It divided shifting state space of attention into concentration and disconvergence, the Markov process is introduced to this paper. The continuous short-term attention state for conversion probability model and a approximate solution were presented. Moreover, according to the long time driving the state conversion point hardly meeting the homogeneous, separated time sessions so the model was generalized. In order to verify the rationality of this model, combination of actual data simulated to test and compared the results with experiment.Here are three modules constructed to identify driving risk status. The core of first module is Bayesian Decision Theory. It aims at minimized classification risk, designing two matrixes to describe lost of wrong judgment based on economy losses and status differences, to obtain a Bayesian risk identification module. Depending on Membership Function Theory in fuzzy mathematics, minimized sum of squares of similar classify errors being objective function, the second module called FCM driving risk status identification is set up. According to owned study sample, the module can be trained by cyclic iteration algorithm. BP neural network driving risk status identification module consists of three input neurons and two output neurons, which is based on Neural Network Theory. Identification precision for each module is tested as mistaken judgment being the index and on the basis of actual data. Finally, some discussions are developed on the application scope of modules. Bayesian risk identification module is suitable for problems based on risk analysis and status similarity classify, while FCM driving risk status identification can only work when talking about problems on status similarity classify and BP neural network driving risk status identification module, which are more intelligent as well as universal, are applicable for the three types problems talked above.Also software of driving risk status identification is developed, which can help to input data, data processing and behaviors' quantification and classification, identify driving risk status.