The Research On Driving Behavior Model And Driver Reliability Analyses

Considering the complexity of traffic information, the probability of drivers'failure has an increment, e.g. drivers'perception, judgment, manipulation, etc., and the vehicle stability control is also becoming less and less. Meanwhile, due to the increasing number and the varying structure of drivers, the reliability of drives during driving is declined. These factors make the high incidence of road traffic accidents, and the number of casualties growing year by year. Therefore, drivers should be the controller of transport system, motor vehicle driver behavior research of road traffic safety has become an important research content. In this paper, the research is based on the driver behavior model and reliability analysis.First, driver's behavior model is researched:A coordinated simulation model of car-following driving is presented. The whole process of driver behavior from the collecting information, analyzing situation and making decisions to controlling vehicle states are coordinated consider. In which, the dynamic traffic information is input; the velocity and acceleration are output. A single nerve cell is used to simulate how the drivers apperceive the changeable information, a fuzzy never network is imposed to extract the eigenvectors of driving behavior as drivers experience, and the fuzzy integral method is applied to describe the way that drivers analyze the information and make decisions.The identification model and precision model of overtaking behavior is discussed, according to 2-demension Hidden Markov Model. In which, the overtaking behavior is considered as the combination of lane change behavior and cut in behavior. So to identify and precise the overtaking behavior is equal to identify and precise the probability of lane change behavior and cut in behavior in time sequence. Considering neither the cognition information which reflect the traffic environment and the subjective information which reflect the driver character could be dispensed, the input of overtaking model is two-dimension. The cognition information includes the velocity difference with the leading vehicle in the objective lane and the difference distance with the leading vehicle in the objective lane. The subjective information is eye movement parameters, including offixation, gaze duration and average eye movement speed. The probability of Overtaking behavior is output. And the Viterbi algorithm and log-likelihood are used together as the solution of the model. Estimation method of rear-ends accidents caused by the delay of controlling behaviors is studied. Supposed the perception of traffic information and the controlling decision of vehicles are correct. The delay of controlling actions is researched in the paper, and the function of driver non-response probability is advanced. Based on the function, the timing curve of divers'response is made. Then the risk model of Rear-ends accidents is established to evaluate the probability of Rear-end accident, according to the theory named ANFIS, whose input are the headway distance, velocity of the following vehicle, the difference of the leading and following vehicle and the non-response probability, whose output is the probability of Rear-end accidents. And the theory on ANFIS could combine the Fuzzy Theory with the Neural Network, learn from their strong points and close the gap. Second, driver reliability is analyzed:Quantitative method about driver reliability is investigated. The influence factors of reliability for drivers in different running stage are analyzed in the paper, firstly, based on Behavior-causing Theory. Then a new quantification method on transience reliability for drivers is advanced by a new definition"confidence degree", whose error rate is calculated by the response time of drivers, and the influence factors how to influent the transience reliability degree is also researched. Based on Hidden Markov Model, a new prediction method on driving reliability is advanced. In which, the velocity of following car, the velocity difference and distance headway is input as observation variables, the driver reliability is output as hidden variable. First the probability of observation states needed and the probability of observation states and driver reliability appeared together is forecasted. Then we could get the prediction value of driver reliability, and give some advices. The warning character of the prediction method could be evaluated not only by accuracy but also by a new index'predictability advanced', which could show the degree of warning time at p probability.Furthermore, all driver models are applied to analyze their impacts. Finally, the future research emphasis is designated.