论文标题
校准微观汽车遵循自适应巡航控制车辆的型号：一种多目标方法
Calibrating microscopic car following models for adaptive cruise control vehicles: a multi-objective approach
论文作者
论文摘要
自适应巡航控制（ACC）车辆是迈向综合车辆自动化的第一步。但是，此类车辆对潜在交通流量的影响尚不清楚。因此，精确地对市售ACC车辆的车辆级动力学进行建模是很有趣的，以便可以将它们用于进一步建模工作，以量化市售ACC车辆对交通流的影响。重要的是，不仅模型选择，而且用于校准的校准方法和误差度量对于准确建模ACC车辆行为至关重要。在这项工作中，我们探讨了如何按照模型来描述ACC车辆动力学的问题。具体而言，我们采用多目标校准方法来了解校准模型参数之间的权衡，以最大程度地减少速度误差与间距误差。为六辆车的数据校准了三种不同的汽车跟随型号。结果与最近的文献一致，并验证靶向较低的间距误差不会损害速度准确性，无论对ACC车辆动力学而不是相反的情况不正确。
Adaptive cruise control (ACC) vehicles are the first step toward comprehensive vehicle automation. However, the impacts of such vehicles on the underlying traffic flow are not yet clear. Therefore, it is of interest to accurately model vehicle-level dynamics of commercially available ACC vehicles so that they may be used in further modeling efforts to quantify the impact of commercially available ACC vehicles on traffic flow. Importantly, not only model selection but also the calibration approach and error metric used for calibration are critical to accurately model ACC vehicle behavior. In this work, we explore the question of how to calibrate car following models to describe ACC vehicle dynamics. Specifically, we apply a multi-objective calibration approach to understand the tradeoff between calibrating model parameters to minimize speed error vs. spacing error. Three different car-following models are calibrated for data from six vehicles. The results are in line with recent literature and verify that targeting a low spacing error does not compromise the speed accuracy whether the opposite is not true for modeling ACC vehicle dynamics.
