Surrounding vehicle behavior identification method based on V2V communication and HMM-GBDT hybrid model

Abstract

The invention discloses a surrounding vehicle behavior identification method based on V2V communication and an HMM-GBDT hybrid model and belongs to the intelligent vehicle driving field. The method comprises steps that a, an offline training link, typical surrounding vehicle behaviors are concluded and divided, for each type of typical behaviors, based on real vehicle platform, the driving information of the surrounding vehicles under real traffic scenarios is collected, trajectory characteristic data is extracted, and parameter learning for the HMM-GBDT hybrid model is carried out. And b, anonline detection link, the acquired self driving information of a tracked target vehicle is transmitted to a driver in real time, a new characteristic observation sequence is constructed by the driverin combination with trajectory characteristic data of two vehicles, and the trained HMM-GBDT hybrid model is utilized to identify belonging behavior modes of the tracked vehicles. The method is advantaged in that the historical trajectory characteristics of vehicle are acquired in a passive information reception mode, influence of the traffic status and environmental factors on active detection is avoided, the method is not dependent on a fixed base station in a common vehicle network system, instant information transmission is guaranteed, and the target vehicle behaviors can be accurately identified.

Description

technical field [0001] The invention belongs to the field of vehicle intelligent driving, and in particular relates to a surrounding vehicle behavior recognition method based on a V2V communication and HMM-GBDT hybrid model. Background technique [0002] In recent years, vehicle behavior recognition by analyzing the characteristics of vehicle historical trajectories has become one of the hot issues that researchers pay attention to. The key to behavior recognition is to learn the behavior pattern of the vehicle, establish a behavior recognition model, and then perform vehicle behavior recognition through the trained vehicle behavior recognition model, and even predict vehicle behavior. [0003] In order to provide training samples for the surrounding vehicle behavior recognition model, it is necessary to use V2V technology to exchange each other's state information to extract vehicle trajectory feature data. V2V technology was first proposed by some American car companies. ...

AI Technical Summary

Problems solved by technology

V2V must also overcome the complex communication environment. The Doppler effect caused by high-speed movement and the complex communication environment will cause serious wireless loss, and the mutual interference of communication between vehicles will also play an important role.

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