基于车联网环境的驾驶员反应时间研究

Research on driver reaction time in Internet of vehicles environment

为探究车联网(IOV)环境下驾驶员反应时间规律,搭建车车互联平台进行实车跟驰实验,基于高斯混合模型(GMM)分析了车联网环境跟驰状态下驾驶员反应时间特征。通过对传统和车联网环境驾驶员反应时间实测数据拟合分布模型对比,验证了车联网环境高斯混合模型优于正态分布和对数正态分布模型,及进一步对车联网环境道路基本通行能力的修正应用。研究结果表明:车联网环境下:1)简单反应时间缩短7.94%,复杂反应时间缩短25.79%,单车道基本通行能力提升15.39%;2)驾驶员反应时间总体标准差降低32.99%,分布更为集中;3)速度对反应时间的影响不显著。

To explore the characteristics of driver reaction time in Internet Of Vehicles（ IOV） environment, a vehicle-tovehicle platform was established to perform real-world driving tests. The characteristics of the following driver＇ s reaction time in IOV environment were analyzed based on a Gaussian Mixture Model（ GMM）. By comparing fitted distribution models of reaction time with the actual testing data in both traditional and networked vehicle environments, it was found that the GMM was superior to the normal distribution model and lognormal model in the IOV environment. The simple reaction time was reduced by 7. 94%, the complex reaction time was reduced by 25. 79%, and single lane basic traffic capacity was improved by 15. 39% in the IOV environment. Furthermore, the total standard deviation of the driver ＇ s reaction time was reduced by 32. 99% and real-time distributed more concentratedly. The effects of speed on reaction time were not significant in the IOV environment.