Rearch On The Algorithm Of Vehicle Trajectory Prediction Based On HMM

In Intelligent Transportation Systems(ITS),logistics distribution and mobile e-commerce,the real-time,accurate and reliable vehicle trajectory prediction has significant application value.Vehicle trajectory prediction can not only provide accurate location-based services,but also can monitor and predict traffic situation in advance,and then further recommend the optimal route for users.In this thesis,firstly,establish road network model,mine the double layers of hidden states of vehicle historical trajectories and then determine the parameters of Hidden Markov Model(HMM)by historical data.Secondly,adopt Viterbi algorithm to seek the double layers hidden states sequences corresponding to the just driven trajectory.Finally,we propose a new algorithm(DHMTP)for vehicle trajectory prediction based on the hidden Markov model of double layers hidden states,and predict vehicle's entire trajectory and the nearest neighbor unit of location information of the next k stages.Due to the relatively limited data,the training set cannot contain all of the vehicle trajectories.When testing the accuracy of prediction,the testing set may be some paths and trajectory points which may not be included in the training set.In addition,the HMM for vehicle trajectory prediction we have previously established exists "zero probability" problem.There are two possible causes,one is the number of some coordinate points in training set appears to zero,the other is some vehicle driving paths or trajectory points don't exist in the training set,but exist in the testing set.So in order to optimize model,we adopt k-means++ clustering algorithm to expand training set and use the smoothing technique to solve the "zero probability" problem.Futher propose Possible_Track algorithm to predict the vehicle trajectory's path.Finally,on the basis of the research for vehicle trajectory prediction method,this thesis presents the path recommendation system framework based on HMM.In a short period of time,it can remind the safety of the driver forward congestion crossing;In a long time,it can also predict the regional where traffic jams will happen and make traffic guide scheduling timely,reminding the driver of making adjustments of routes in time.