Vehicle behavior identification method

Abstract

The invention relates to the field of vehicle intelligent perception, and discloses a vehicle behavior identification method. The method comprises the steps of obtaining the current transverse distance of a front vehicle relative to a main vehicle and the first duration of transverse distance change of the front vehicle; wherein the front vehicle is a vehicle which runs in front of the host vehicle and is adjacent to the host vehicle, and the front vehicle and the host vehicle run on different lanes; determining a first lane changing two-degree-of-freedom chi-square value of the front vehicle based on the current transverse distance and the first duration, and determining a first lane changing probability of the front vehicle relative to a lane where the host vehicle is located based on the first lane changing two-degree-of-freedom chi-square value; and based on the first lane changing probability and a first set lane changing probability threshold value, identifying the lane changing behavior of the front vehicle. According to the method, the cut-in and cut-out behaviors of the vehicle are identified by using the chi-square distribution of two degrees of freedom, and the vehicle behavior identification efficiency is improved on the basis of ensuring the vehicle behavior identification accuracy.

Description

technical field [0001] The invention relates to the field of vehicle intelligent perception, in particular to a vehicle behavior recognition method. Background technique [0002] For intelligent driving systems such as automatic driving and assisted driving, it mainly makes decisions based on the real-time status information of the surrounding objects provided by various sensors on the vehicle. The system cannot predict the upcoming behavior in advance, especially for the front vehicle. In extremely dangerous scenarios such as sudden cut-in and sudden deceleration at close range, the system will be greatly restricted. Based on this, some methods of automatic recognition of vehicle behavior have emerged in order to predict the upcoming behavior of the vehicle. [0003] However, the current vehicle behavior recognition methods are mainly implemented based on deep learning algorithms, which not only need to collect a large amount of data to train the model, but also require th...

AI Technical Summary

Problems solved by technology

[0003] However, the current vehicle behavior recognition methods are mainly implemented based on deep learning algorithms, which not only need to collect a large amount of data to train the model, but also require the system to have strong computing power, which will result in high system implementation costs and also due to The model is more complex and affects the prediction efficiency of vehicle behavior

Images