Model-Based String Stability of Adaptive Cruise Control Systems Using Field Data

Abstract

This article is motivated by the lack of empirical data on the performance of commercially available Society of Automotive Engineers level one automated driving systems. To address this, a set of car following experiments are conducted to collect data from a 2015 luxury electric vehicle equipped with a commercial adaptive cruise control (ACC) system. Velocity, relative velocity, and spacing data collected during the experiments are used to calibrate an optimal velocity relative velocity car following model for both the minimum and maximum following settings. The string stability of both calibrated models is assessed, and it is determined that the best-fit models are string unstable, indicating they are not able to prevent all traffic disturbances from amplifying into phantom jams. Based on the calibrated models, we identify the consequences of the string unstable ACC system on synthetic and empirical lead vehicle disturbances, highlighting that commercial ACC platoons of moderate size can dampen some disturbances even while being string unstable. The primary contributions of this article are the development of a data-driven approach to calculate string stability of ACC systems, and the collection and interpretation of a dataset to understand the car following behavior of a commercial ACC system.