Autoregressive State Prediction Model Based on Hidden Markov and the Application

摘要

Considering the inaccuracies of the traditional Hidden Markov Model (HHM) in the dynamic processes that are close relatively related before and after characterization, an autoregressive state prediction model based on Hidden Markov with Autoregressive model and the coefficient of AR is proposed, which takes the coefficient of AR as the observations of the continuous HHM. Taking the recognition and prediction of heavy vehicle driving states as the research object, a two-layer HMM model is set up to describe the state of the whole steering process of the vehicle. The AR model is for the features extracting of the observations in a short period of time, and the coefficient of AR is extracted as the observed sequence of the lower HMM model library. The upper HMM is used to identify and predict the overall state of the vehicle during steering. The proposed model makes the state sequence with the highest probability on-line predicted in the observed sequence by the Viterbi algorithm, and calculates the state transition law to predict the state of the vehicle in a certain period of time in the future using the Markov prediction algorithm. Combining the double lane change and hook steering to train the parameters of the model, the online identification and prediction of heavy vehicle rollover states can be achieved. The results show that the proposed model can accurately identify the driving state of the vehicle with good real-time performance, and the good prediction on the trend of heavy vehicle driving conditions is verified.