Research On Key Technologies Of Traffic Information Distribution And Processing In VANET Environment

With the rapid development of VANET technology, VANET applications have been developed from the primary stage of entertainment and navigation services as the main content to travel guidance, energy saving driving as a sign of the intermediate stage. At present, the VANET applications have entered the V2X-based coordination control as the main research content of the advanced stage. At this advanced stage, with the increasingly mature of 5G mobile communication technology(5G's most important application scenario is VANET), the development and application of the sophisticated cloud computing and super big data real-time processing technology, centralized(scattered vehicle travel information is collected to the data center.) and V2 X communication based safety control technology is bound to become a new hot spot in the field of VANET research. In the evolution process of the VANET, the traffic information distribution and processing technology, which is marked by the position of vehicles, will face new challenges.In VANET environment, communication network, location network and road traffic network overlay each other, which bring great difficulties to the distribution and processing of traffic information. In this paper, the key technologies of traffic information distribution and processing are studied based on vehicle position, vehicle path prediction, information generation, information transmission, information processing and information usage. The main research contents are as follows.(1) Vehicle positioning and path prediction technology in VANETAccurate vehicle positioning and path prediction are the base of traffic information. Based on the analysis of GPS/DR combined positioning theory and Kalman random path prediction technique, the stochastic path BM-KFFP prediction model based on BM model is proposed. Through the actual driving test, the BM-KFFP prediction model can provide data source support for traffic information generating.(2) Traffic information transmission support technology based on DSRCIn the high density communication environment, the performance degradation, saturation and congestion of wireless communication are caused by a large number of traffic information. The most forward-looking strategy is to block the channel congestion at the first time, which requires the vehicle to be able to predict the entire channel load at the next moment. Firstly, based on the analysis of the main application scenarios of DSRC in VANET by classification of V2 X information dissemination way, it is determined that beacon messages are the main carrier of the vehicle position information, but also wireless channel saturation and congestion. Then, by examining the relationship between channel load and its influence factors of time series, a grey correlation analysis method is proposed to select the main factors of affecting the channel load. Through the selected main influence factors of channel load, the multiple-regression Kalman filter channel load prediction algorithm is proposed. Finally, to verify the effectiveness of the channel load prediction algorithm, a traffic survey method was employed to conduct a short-term channel load forecasting experiment on a segment of urban road by using a floating car.On the basis of prior channel load prediction, the premise of communication connectivity and node transmission fairness, through a pre-defined maximum and minimum threshold, a beacon transmission power control algorithm based on channel load forecasting and comparing is established. Then, the effectiveness of the algorithm was verified by the simulation experiment of the urban road intersection and the eight-lane highway. Finally, taking into account the impact of vehicle distance, occlusion and other factors on the signal transmission in the real road environment, the algorithm was verified again by using the measured data by floating cars.(3) Traffic information generation and processing technology in VANETTo solve storage and updating of high density traffic information, the key is to reduce the updating frequency of the vehicle position data, so as to reduce the load of the database. Firstly, based on the relationship between the prediction accuracy and the time horizon of Kalman filter, the position updating model of "prediction and comparison" is designed. On the basis of this model, a new vehicle position updating strategy is proposed. Secondly, to reduce the loss of packet delivery ratio in the process of communication transmission, a transmission mode decision algorithm is designed by improving the updating model, which is based on the distance of the adjacent vehicle. Finally, the database updating strategy and transmission mode decision algorithm were verified by the eight-lane highway simulation and actual urban road experiment.In VANET, On the premise of meeting the requirement of vehicle position accuracy and wireless communication performance, research and design of the beacon message generation strategy is needed. Firstly, according to the Kalman filter difference prediction equation, the beacon generation model is established. The beacon message generating model and strategy based on time interval adaptive adjusting are established according to the measured channel load and the preset threshold. Traffic data from the simulation of eight-lane highway basic segment and the floating car driving test on actual urban road was collected to verify the second model and strategy. Finally, a beacon missing data filling algorithm is proposed based on least squares support vector machine, the effectiveness of the algorithm was verified by an actual example.(4) Vehicle position data processing and dynamic route guidance methodIn VANET data center, the central travel route guidance can carry out by using position data from all the scattered vehicles. First, this paper introduces the processing method of the position data, including the quality evaluation and control method, the road connectivity analysis, the error analysis and the vehicle parking identification method. Then, the single-vehicle and road-section travel time estimation method is put forward by employing vehicle position data, which is sampled and transmitted by interval adaptive adjustment in large vehicles and high density communication environment. The validity of the method was proved by comparing the actual road travel time. Finally, on the basis of travel time estimation method, this paper presents a dynamic route guidance method in VANET environment. The simulation results show the method is effective.