Model based string stability of adaptive cruise control systems using field data
For traffic engineers and developers of automated driving systems, this work provides empirical evidence that current commercial ACC systems are string unstable, which has implications for traffic flow stability.
This study calibrates car-following models for a commercial adaptive cruise control system using field data and finds that the best-fit models are string unstable, meaning they cannot prevent all traffic disturbances from amplifying into phantom jams. However, some disturbances can be dampened even with string unstable ACC platoons of moderate size.
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 some disturbances can be dampened even with string unstable commercial ACC platoons of moderate size.