摘要
提出一种低渗透率智能网联环境下高风险事件预警方法。具体而言,基于熵能表征系统状态的特点提出交通熵的概念,将个体车辆的微观驾驶行为量化为交通熵,以表征交通流状态;再将交通熵作为长短时记忆网络模型(Long Short⁃term Memory,LSTM)的输入参数建立预警模型;最后,使用HighD轨迹数据集提取高风险事件,并验证模型有效性。结果显示,使用交通熵的模型误报率和漏报率大幅降低。以智能车渗透率10%为例,误报率和漏报率分别从6.18%和11.47%下降到了1.95%和3.12%;在预测模式下,对高风险事件误报率和漏报率为2.28%和3.82%。
We propose an early warning method for high-risk events of traffic operation under low penetration of connected and autonomous vehicles(CAVs).Specifically,we first define the concept of traffic entropy,and quantifies the micro driving behavior of individual vehicles as a parameter represented by traffic entropy,which is used to characterize the state of macroscopic traffic flow.And then the traffic entropy is used as the input parameter of the Long Short-Term Memory(LSTM)model to establish the early warning model of high-risk events.The HighD Dataset from German highways was utilized for the empirical analyses.In order to compare the application results under CAVs environment,an autonomous-vehicles scenario and a connected-vehicles scenario were set for the high-risk events and non-risk events extracted from the HighD Dataset.and the effectiveness of the warning of high-risk events under different vehicle permeability was compared.Results show that,the false alarm and missed alarm rates of early warning model with traffic entropy parameters are both reduced.Taking the low-penetration CAVs of 10%as an example,the false alarm and missed alarm rates reduced from 6.18%and 11.47%to 1.95%and 3.12%,respectively.At the same time,the false alarm and missed alarm rates are only 2.28%and 3.82%under the prediction environment.
作者
陈晓芸
叶颖俊
余荣杰
孙剑
CHEN Xiaoyun;YE Yingjun;YU Rongjie;SUN Jian(Key Laboratory of Road and Traffic Engineering of the Ministryof Education,Tongji University,Shanghai 201804,China)
出处
《同济大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2023年第10期1595-1605,共11页
Journal of Tongji University:Natural Science
基金
国家重点研究发展计划(2018YFB1600505)
国家自然科学基金重点项目(52125208)
浙江省重点研发计划(2021C01011)。
关键词
交通安全
智能网联交通
高风险事件
交通熵
预警模型
低渗透率
traffic safety
connected and autonomous vehicles(CAVs)
high-risk event
traffic entropy
early-warning model
low penetration
作者简介
第一作者:陈晓芸(1993—),女,工学博士,主要研究方向为交通运行建模与仿真、驾驶行为研究。E-mail:1610753@tongji.edu.cn;通信作者:孙剑(1979—),男,教授,博士生导师,工学博士,主要研究方向为交通流理论与仿真,智能网联汽车与车路协同。E-mail:sunjian@tongji.edu.cn。
