摘要
深入研究人类驾驶员的驾驶行为和习性,对于推进智能汽车的拟人化决策规划,改善驾驶安全性具有重要意义。针对高速公路这一典型场景,基于NGSIM(Next Generation Simulation)数据集提取有效表征换道驾驶行为的特征参数,分析换道驾驶行为与驾驶参数的相关性,量化驾驶行为特性,建立了基于高斯混合-隐马尔科夫理论(Gaussian mixed model-hidden Markov model,GMM-HMM)的换道意图识别模型。研究结果表明:该模型识别准确率较高,在换道点1.0 s之前的换道行为识别准确率达到95.6%,在有换道意图的时刻识别准确率超过80%,可应用于智能汽车换道策略的拟人化设计,有效降低换道风险,改善驾驶安全。
Understanding human driving behaviors has significant implications for promoting decision-making in intelligent vehicles and improving driving safety.This study focuses on highway lane-changing behavior,using the NGSIM(Next Generation Simulation)Dataset to extract key parameters and analyze the correlation between these parameters and driving behaviors.A GMM-HMM-based model for lane-changing intention recognition was developed,achieving an accuracy of 95.6%in predicting lane changes 1.0 s before they occur,and an accuracy of over 80%in recognizing lane-changing intentions.This model can be applied to intelligent vehicle design to effectively reduce lane-changing risks and improve driving safety.
作者
杨崇辉
郑玲
左益芳
王勘
曾杰
丁雪聪
YANG Chonghui;ZHENG Ling;ZUO Yifang;WANG Kan;ZENG Jie;DING Xuecong(State Key Laboratory of Mechanical Transmissions,Chongqing University,Chongqing 400044,P.R.China;China Merchants Testing Vehicle Technology Research Institute Co.,Ltd.,Chongqing 400067,P.R.China)
出处
《重庆大学学报》
CAS
CSCD
北大核心
2024年第11期37-50,共14页
Journal of Chongqing University
基金
国家自然科学基金资助项目(51875061)。
关键词
驾驶员特性
换道行为分析
NGSIM
驾驶安全
driver characteristics
lane-changing behavior analysis
NGSIM(Next Generation Simulation)
driving safety
