Vehicle lane change behavior detection method based on machine learning

被引：0
|
作者：
Yan X.T. [1 ]
Shang Z.L. [2 ]
机构：
[1] School of Information Technology, Luoyang Normal University, Luoyang
[2] Engineering Trainning Center, Zhengzhou University of Light Industry, Zhengzhou
来源：
Advances in Transportation Studies | 2023年 / 1卷 / Special Issue期
关键词：
areas of interest; behavior detection; forced lane change of vehicles; grayscale; kalman filtering; machine learning;
D O I：
10.53136/97912218061513
中图分类号：
学科分类号：
摘要：
In this paper, a vehicle forced lane change behavior detection algorithm based on machine learning is proposed. Collect the vehicle's forward looking monocular video image and determine the reasonable region of interest: The intention and process of vehicle forced lane change are analyzed by video image features, and the vehicle forced lane change motivation model is established; Use Kalman filter in machine learning to track vehicles: Calculate the centroid coordinates of the target vehicle and the distance of the lane line, and determine whether the vehicle in front has a forced lane change behavior in combination with the lane change motivation model. The experimental results show that this method has higher accuracy in detecting vehicle lane change behavior. © 2023, Aracne Editrice. All rights reserved.
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页码：27 / 38
页数：11
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