Car-Following Characteristics of Adaptive Cruise Control from Empirical Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 9
Abstract
Computer-driven vehicles will behave differently from human-driven vehicles due to changes in perception abilities, precision control, and reaction times. These changes are expected to have profound impacts on capacity, yet few models of automated driving are based on empirical measurements of computer-driven vehicles in real traffic. To this end, this paper investigates characteristics of an early form of longitudinal control automation, a commercially available adaptive cruise control (ACC) system driven in real traffic. Two car-following models were calibrated to a vehicle with ACC. First, the Intelligent Driver Model was reformulated to comply with ACC design standards then calibrated to match speed and range data from the test vehicle. The vehicle with ACC was found to decelerate less severely than predicted by the model when tested in severe braking and unimpeded acceleration scenarios. Second, the Wiedemann 99 model was calibrated because it is the default car-following model in the traffic microsimulation software program Vissim and can therefore be implemented cheaply and quickly in sophisticated models of roadways worldwide. Four parameters of the Wiedemann 99 model were measured directly from field observations of the test vehicle: standstill distance, start-up time, unimpeded acceleration profile, and maximum desired deceleration. Simulation results in Vissim were found to match the adaptive cruise control in unimpeded acceleration tests. These findings will benefit researchers and modelers seeking more accurate models of car-following behavior with adaptive cruise control and automated longitudinal control.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Available data include speed/acceleration/gap measurements, Vissim trajectories and models, and IDM calculations.
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. In-car video may be shared in short, anonymized sections to protect identities of other road users.
Acknowledgments
This work was sponsored by the Virginia Department of Transportation. The authors thank Eun (Tina) Lee for her assistance with data collection.
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© 2020 American Society of Civil Engineers.
History
Received: Dec 6, 2019
Accepted: May 4, 2020
Published online: Jul 7, 2020
Published in print: Sep 1, 2020
Discussion open until: Dec 7, 2020
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