期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 22, 期 11, 页码 6992-7003出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.3000682
关键词
Stability analysis; Mathematical model; Cruise control; Analytical models; Adaptation models; Automobiles; Data models; Adaptive cruise control; string stability
资金
- Inria Associated Team MEMENTO Modeling autonomous vehicles in traffic flow
- National Science Foundation [OISE-1743772, CNS-1446435, CNS-1446690, CNS-1446715, CNS-1446702]
- IDEX-IRS 2018 Project MAVIT''
This article assesses the string stability of seven 2018 model year vehicles equipped with ACC, finding that all models have string unstable ACC systems. Validation of this finding is done through driving experiments, with the data collected representing the largest publicly available comparative driving dataset on ACC equipped vehicles.
In this article, we assess the string stability of seven 2018 model year adaptive cruise control (ACC) equipped vehicles that are widely available in the US market. Seven distinct vehicle models from two different vehicle makes are analyzed using data collected from more than 1,200 miles of driving in car-following experiments with ACC engaged by the follower vehicle. The resulting dataset is used to identify the parameters of a linear second order delay differential equation model that approximates the behavior of the black box ACC systems. The string stability of the data-fitted model associated with each vehicle is assessed, and the main finding is that all seven vehicle models have string unstable ACC systems. For one commonly available vehicle model that offers ACC as a standard feature on all trim levels, we validate the string stability finding with a multi-vehicle homogeneousplatoon experiment in which all vehicles are the same year, make, and model. In this test, an initial disturbance of 6 mph is amplified to a 25 mph disturbance, at which point the last vehicle in the platoon is observed to disengage the ACC. The data collected in the driving experiments is made available, representing the largest publicly available comparative driving dataset on ACC equipped vehicles.
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