Modeling Adaptive Cruise Control Vehicles: A Continuous Asymmetric Car-Following Perspective

Abstract

Adaptive cruise control (ACC) vehicles are the first generation of automated vehicles. Studying the traffic impacts of commercially available ACC vehicles is an initial and crucial step before fully automated vehicles become widely available in the market. Many studies focus on modeling the behavior of ACC vehicles with a microscopic car-following model where a single continuous function has been mostly used to describe vehicle acceleration. This may exhibit unrealistic driving behavior. A recent study has developed an asymmetric optimal velocity relative velocity (AOVRV) model to describe both accelerating and braking driving behaviors of ACC vehicles. However, this model is inadequate in fully capturing realistic vehicle acceleration, leading to increased errors in modeling vehicle velocity. To address this issue, a continuous asymmetric car-following (CAOVRV) model is proposed to incorporate a hyperbolic tangent function for producing more realistic vehicle acceleration that can better capture the driving behavior of commercially available ACC vehicles. Both the AOVRV model and the proposed CAOVRV model are calibrated with an experimental ACC dataset to assess their performance. With the calibrated parameter values, we simulate vehicle trajectories and compute velocity errors against experimental data. The results show that the proposed CAOVRV model is capable of producing more realistic vehicle acceleration and can capture the driving behavior of ACC vehicles with a higher degree of accuracy compared to the AOVRV model. As a result, velocity errors are reduced by up to 11%. The proposed car-following model allows for simulating ACC vehicle behavior more accurately, contributing towards the capturing of traffic flow dynamics.