Target vehicle lane changing behavior self-adaptive identification model construction method

Abstract

The invention discloses a target vehicle lane changing behavior self-adaptive identification model construction method, which comprises the following steps of 1, establishing a lane changing behaviorinfluence parameter set, and dividing samples to obtain model input; 2, based on the large-scale vehicle trajectory data set, conducting training to obtain a general model for identifying left and right target lane changing behaviors; and 3, establishing an adaptive lane changing behavior recognition model based on Bayesian inference. According to the target vehicle lane changing behavior self-adaptive identification model construction method, the lane changing behaviors of surrounding target vehicles during driving can be effectively identified through setting of the steps 1 to 3 so that information support can be provided for driving behavior decision of the vehicles, and thus driving safety can be enhanced.

Description

technical field [0001] The invention relates to the field of automatic driving research, in particular to a method for constructing an adaptive identification model for lane-changing behavior of a target vehicle. Background technique [0002] The number of automobiles in our country continues to increase, and the problem of traffic safety cannot be ignored. During the driving process of the vehicle on the road section, the behavior of changing lanes will generate more traffic conflicts than the behavior of following the car, and the information that the driver needs to process is more complicated, and the surrounding vehicles generally have irregular use of turn signals, which makes Lane changing behavior can easily cause traffic accidents, causing casualties and property losses. At present, advanced driving assistance systems such as lane-changing assistance systems and collision warning systems have been widely used in mass-produced vehicles. Changes in vehicle motion be...

AI Technical Summary

Problems solved by technology

In the existing lane-changing behavior recognition model, the driver’s physiological information such as eye movement and head movement collected by the camera in the car, and the vehicle state information such as the steering wheel angle collected by OBD and other equipment have a great effect on the recognition of the driving behavior of the own car. , and in the current situation where connected vehicles are not yet popularized, it is unable to detect key information such as the physiological characteristics of surrounding vehicle drivers

At the same time, existing studies mostly use large-scale data sets for supervised learning of lane-changing behavior, which takes a long time to learn and has weak applicability in complex and changeable road environments.

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