摘要
准确识别驾驶行为,有利于提高车辆行驶安全性。文章针对目前驾驶行为识别方法的主观性和识别准确率低的问题,从驾驶员的角度出发,建立了基于门控循环单元网络的驾驶行为辨识模型。利用驾驶模拟器获取驾驶员操纵数据并建立驾驶行为数据集,模型经过训练后,能够有效利用驾驶员操纵数据的时序特征,成功识别紧急加速、紧急制动和平稳直线行驶,模型的识别准确率到达96.67%,为交通安全领域提供重要的理论支持。
Accurate recognition of driving behavior plays an important role in vehicle driving safety.Aiming at the subjectivity and low recognition accuracy of current driving behavior recognition methods,this paper establishes a driving behavior recognition model based on the gated recurrent unit.The driving simulator is used to obtain driver manipulation data and establish a driving behavior data set.After the model is trained,it can effectively use the timing characteristics of the driver manipulation data to successfully recognize driving behavior.The recognition accuracy of the model reaches 95.24%,which provides important theoretical support for the field of traffic safety.
作者
薛俊俊
陈双
XUE Junjun;CHEN Shuang(College of Automotive and Transportation,Liaoning University of Technology,Liaoning Jinzhou 121000)
出处
《汽车实用技术》
2021年第24期30-33,共4页
Automobile Applied Technology
基金
辽宁省高等学校创新人才计划。
关键词
驾驶行为辨识
深度学习
门控循环单元
Driving behavior recognition
Deep learning
Gated recurrent unit
