Existing studies had shown that advanced driver assistance systems (ADAS) and driver individual characteristics can significantly affect driving behavior. Therefore, it is necessary to consider these factors when building the car-following model. In this study, we established a car-following model based on risk homeostasis theory, which uses safety margin (SM) as the risk level quantization parameter. Firstly, three-way Analysis of Variance (ANOVA) was used to analyze the influencing factors of car-following behavior. The results showed that ADAS and driving experience have a significant effect on the drivers’ car-following behavior. Then, according to these two significant factors, the car-following model was established. The statistical method was used to calibrate the parameter reaction response τ. Other four parameters (SM_DL, SM_DH, α₁, and α₂) were calibrated using a classical genetic algorithm, and the effects of ADAS and driving experience in these four parameters were analyzed using T-test. Finally, the proposed model was compared with the GHR model, and the result showed that the proposed model has a smaller Root Mean Square Error (RMSE) than the GHR model. The proposed model is a method of simulating different driving behaviors that are affected by ADAS and individual characteristics. Considering more driver individual characteristics, such as driving style, is the future research goal.

中文翻译：

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考虑ADAS和驾驶经验的基于安全裕度的乘车模型

现有研究表明，高级驾驶员辅助系统（ADAS）和驾驶员个人特征会严重影响驾驶行为。因此，在建立跟车模型时必须考虑这些因素。在这项研究中，我们基于风险动态平衡理论建立了汽车跟随模型，该模型使用安全裕度（SM）作为风险水平量化参数。首先，运用三方差分析（ANOVA）分析了跟车行为的影响因素。结果表明，ADAS和驾驶经验对驾驶员的跟车行为有显着影响。然后，根据这两个重要因素，建立了跟车模型。统计方法用于校准参数反应响应τ。其它四个参数（SM _DL，SM _DH，α ₁和α ₂），使用经典遗传算法进行了校准，以及使用分析的ADAS在这四个参数的影响和驾驶体验Ť -test。最后，将该模型与GHR模型进行了比较，结果表明该模型比GHR模型具有更小的均方根误差（RMSE）。提出的模型是一种模拟受ADAS和个人特征影响的不同驾驶行为的方法。未来的研究目标是考虑更多驾驶员个性特征，例如驾驶风格。