Prediction method of driving risk based on hidden Markov model

Abstract

The invention discloses a prediction method of driving risk based on a hidden Markov model. The method comprises the steps of (1) classifying driving risk states through a cluster analysis method based on vehicle operating characteristics, (2) estimating the influences of a driver behavior and surrounding traffic environment characteristics on a transition probability between driving risk states through multiple logistical models for different driving risk states, (3) with a risk state as a hidden state, with an actual observed vehicle movement variable as a state output value, with multiple logistic model parameters as parameter initial values of a state transition probability matrix, establishing a hidden Markov chain model that reflects the evolution rule of driving states, and (4) obtaining the vehicle operating characteristics in real time and predicting a future risk state based on the hidden Markov chain model. According to the method, the hidden Markov model which can reflect the above characteristic real-time change and has a variable state transition probability is established, the accuracy and prediction accuracy of a driving risk model are improved, and the real-time requirements of anti-collision warning can be satisfied.

Description

technical field [0001] The invention relates to the technical field of traffic safety evaluation and intelligent traffic system active safety, in particular to a method for predicting driving risk based on a hidden Markov model. Background technique [0002] The increase of car ownership and the rapid development of the road transport industry have brought increasing pressure on the road traffic safety environment while prospering the economy and facilitating people's lives. The average annual death toll in traffic accidents in my country ranks first in the world, and road traffic accidents have become the field with the largest number of deaths in China's production safety. According to a number of research reports, if the driver can realize the risk of accident 0.5s earlier and take corresponding correct measures, 50% of accidents can be avoided; if it is 1s earlier, 90% of accidents can be avoided. Therefore, vehicle active safety technology and system development has be...

AI Technical Summary

Problems solved by technology

In fact, the entire process from the formation of driving risks to the occurrence of dangerous conflicts is difficult to describe with a single early warning parameter, and more complex algorithms and models are needed for research

At the same time, the existing early warning model algorithms usually only consider the operating characteristics of the vehicle (such as speed characteristics and acceleration characteristics), while ignoring the impact of real-time driver behavior, road and environmental changes on the driving risk state, and cannot fully describe the driving state. The internal evolution law of time is not conducive to the accuracy and prediction accuracy of the driving risk model

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