The Research Of Rear-end Collision Alarm Model Based On MAS And Driving Behavior

The establishment of a sound system of road safety precautions is an endeavor of great social significance with an impact on the ensuring and improvement of the national economy and the people's livelihood.According to related statistics,the rear-end collision(RC) is one of the major forms of traffic accidents,accounting for 23%of all the collision accidents.While with the help of the pre-warning system, the number of RC could be reduced by over 62%.So far,most automobiles have not been equipped with the rear-end collision alarm system(RCAS),which,therefore, promises a golden prospect for the development and application of the RCAS.Based on the analysis of the causes of RC of traffic accidents,this paper dedicated to model the relationship between rear-end collision and driving behavior according to the multi-agent system theory.Driving behavior is characterized by complexity and uncertainty as it is more often than not influenced by multi-factors. So the current study falls into the field of Intelligent Transportation System(ITS) which is the primary concern of the study of the active safety precaution.Since the driver is the subject of road traffic,his/her behaviors play an important role in the study of RCAS.It is self-evident that traditional software architecture and methods can hardly satisfy the current development of Forecasting Support System(FSS). However,the multi-agent system theory provides new methods for the study and development of FSS.The main contributions of this paper are listed as follows:Firstly,upon the analysis of genetic algorithm(GA) and ant colony optimization(ACO),HAGA,a new method to solve agent coalition problem which based on the combination of GA and ACO,is presented.The purpose of agent coalition is to confirm the major factors affecting DB so as to reduce the time of multifactor comprehensive evaluation and to eliminate contradiction factors.Secondly,through an intensive study of DB,a learning algorithm of DB—DNNIA is proposed,Which is supposed to realize the learning of DB's habits and the forecasting of DB in particular environment.A simulation experiment concerning the study of DB is conducted in such aspects as the pedal opening and the time length when the driver is stepping on the braking or acceleration pedal,as well as the rotation angle and time length of the steering wheel.The simulation results,which fully embody the driver's driving traits,are consistent with the overall tendency of the sample data.Last but not least,by means of Bayes decision theory,a model of car rear-end warning based on MAS and behavior(MCRMASB) and the corresponding decision algorithm are proposed,which is centering on DB and is having MAS as its framework.The aim is to make full use of the characteristics of the agents in MAS such as interaction,autonomy and real-time to embody driver' subjectivity in road traffic and to improve the uncertainty of information in every aspect of RCAS.In this way,the current research attempts to provide a new idea for the study of RCAS.