巡航控制
计算机科学
校准
巡航
车辆动力学
流量(计算机网络)
智能交通系统
模拟
期限(时间)
数据建模
控制(管理)
控制理论(社会学)
汽车工程
工程类
人工智能
航空航天工程
数学
统计
土木工程
物理
数据库
量子力学
计算机安全
作者
George Gunter,Raphael Stern,Daniel B. Work
标识
DOI:10.1109/itsc.2019.8917347
摘要
This article uses experimentally collected car following data from seven different commercially available adaptive cruise control (ACC) vehicles to calibrate microscopic models for each system's car following behavior using three different common car following models. Calibration is conducted by selecting the model parameters that minimize the error between the simulated vehicle trajectories and the experimental data. The goal of this study is two-fold: (i) assess which car- following models typically used to describe human driving behavior are best for describing ACC car-following dynamics, and (ii) provide best-fit calibrated car following models for seven different commercially available ACC vehicles, which can be used to understand the traffic flow impact of ACC systems via simulation analysis. We find that the intelligent driver model and the optimal velocity model with a relative velocity term perform best, and with similar performance to one another, while the Gazis-Herman-Rothery model as calibrated does not capture all the ACC car following dynamics.
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