Highlights Propose a calibration framework for autonomously-driven vehicles to capture the multi-behavioral patterns. Employ two prevailing ACC datasets from OpenACC database. Calibrate two state-of-the-art car-following models, i.e., IDM and AOVRVM, within the proposed framework. Test six typical behavioral patterns, i.e., spacing, time gap, response time, string stability, energy consumption and traffic hysteresis. Compare the performance of the proposed framework with the state-of-the-art framework, namely, calibration on spacing, speed and acceleration.
Abstract Calibration of car-following (CF) models is considered a very important task towards reproduction of individual vehicle behaviors and collective traffic phenomena. In most works, calibration is performed on trajectories of leading and following vehicles either on speed or spacing quantities, with the combination of the two plus the acceleration quantity to be considered as the most appropriate according to the literature. With the advent of adaptive cruise control (ACC) technology, there are intrinsic behavioral differences between human- and ACC-driven vehicles that are visible in experimental observations. Some examples include constant headway for ACC-enabled vehicles, string instability, hysteretic behavior, human-like response time and others. Since calibration is performed only on basic vehicle dynamics, i.e., the spacing, speed and acceleration, even after proper parametrization we cannot be certain that CF models will be able to reproduce some or all of the above phenomena and to what extend. The aim of this work is to propose a multi-objective calibration framework validated on empirical observations of car platoons. Furthermore, it investigates if, and to what extent, the traffic dynamics and behavioral patterns of ACC-driven vehicles can be reproduced. Two state-of-the-art CF models are tested with the empirical dataset. The results indicate that the proposed framework leads to acceptable errors and comparable performance, facilitating the models to capture phenomena in a way that is not possible until now, using the calibration on basic vehicle dynamics.
A multi-objective calibration framework for capturing the behavioral patterns of autonomously-driven vehicles
2023-04-21
Article (Journal)
Electronic Resource
English