Development, Calibration, and validation of a Novel nonlinear Car-Following Model: Multivariate piecewise linear approach for adaptive cruise control vehicles

Abstract

As advanced driver-assistance systems (ADAS) are more widely implemented—particularly the adaptive cruise control (ACC) system in car-following behavior—accurate modeling of ACC vehicles’ behavior is needed. Currently, each vehicle manufacturer develops its ACC systems with unique controllers, creating “black boxes” that are not openly accessible for analysis. In addition, the vehicle dynamics that use sensor and dynamics systems introduce complicated inherent nonlinearity in ACC-equipped vehicles’ behaviors (hereinafter referred to as ACC vehicles). Given the complex inherent nonlinearity of ACC systems, traditional vehicle behavior modeling methods have to trade off between modeling accuracy and interpretability. To address these challenges, this study introduces an innovative multivariate piecewise linear (MPL) car-following modeling methodology to emulate any ACC system from observational data. The MPL modeling methodology allows for several empirical quantifications of the unknown design parameters of the different ACC systems as they are manifested in the vehicles’ driving behavior. This approach distinguishes itself from traditional piecewise linear models by incorporating multiple breakpoints. These breakpoints serve to capture the inherent nonlinearity of ACC vehicle dynamics across a range of linear functions. Simultaneously, these breakpoints are empirically derived from actual trajectory data, thereby enhancing modeling accuracy. A case study illustrates four calibrated MPL models based on a large-scale field automated vehicle experiment dataset. Compared to other models, the MPL models show sufficient accuracy and interpretability. This robust and transparent modeling methodology may serve as an analytical tool for future transportation planning.