Judgment and prediction method of driving behavior intention

Abstract

The present invention relates to the field of traffic safety, in particular to a judgment and prediction method of driving behavior intention based on the implicit Markov model (HMM), aiming to overcome the defect that the existing driving behavior intention recognition and prediction technology does not take into account the dynamics and continuity of driving behavior, as well as complex behaviors such as lane changing, car following and braking and the like. The judgment and prediction method of driving behavior intention obtains time series segmentation data from cluster analysis of dynamic driving data, the linear direction HMM, lateral HMM and speed classification model are trained respectively, and the obtained identification results are regarded as the observation sequence of behavior recognition layer; Then, off-line training is performed to deal with normal or emergency braking, normal or emergency lane change, normal or dangerous driving behavior, and multi dimensional discrete HMM model; according to the model parameters and the observation sequence, the next time step driving behavior can be predicted. The judgment and prediction method of driving behavior intention takes the complexity and continuity of driving behavior into account and can dynamically judge and predict the driving behavior intention and warn the dangerous behavior, and accordingly can be applied to driving behavior evaluation and driving assistance system.

Description

technical field [0001] The invention relates to the field of traffic safety, in particular to a method for judging and predicting driving behavior intentions based on a hidden Markov model (Hidden Markov Model, HMM). Background technique [0002] With the rapid increase of the number of motor vehicles, traffic safety accidents remain high. A large number of studies have shown that driver misbehavior is the main cause of traffic accidents, and driving behavior directly affects road capacity and traffic safety. Therefore, it is of great practical significance to study the identification and prediction of driving behavior intentions. [0003] Existing research focuses on two aspects. On the one hand, from the psychological point of view of drivers, discrete choice models (such as Logistic models) are used to describe driver behavior. On the one hand, it mainly focuses on the static identification of driving intentions, such as using fuzzy reasoning to identify driving intent...

AI Technical Summary

Problems solved by technology

This invention adopts NAR neural network, and introduces driving intention and vehicle speed time series together as the input of the network. Although this invention optimizes the multi-step dynamic vehicle speed prediction effect, it uses traditional fuzzy recognition in driving intention identification, which fails to reflect Dynamics and continuity of driving behavior

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