Modeling Adaptive Cruise Control (ACC) vehicles enables the understanding of the impact of these vehicles on traffic flow. In this work, two online methods are used to provide real time system identification of ACC enabled vehicles. The first technique is a recursive least squares (RLS) approach, while the second method solves a nonlinear joint state and parameter estimation problem via particle filtering (PF). We provide a parameter identifiability analysis for both methods to analytically show that the model parameters are not identifiable using equilibrium driving. The accuracy and computational runtime of the online methods are compared to a commonly used offline simulation-based optimization (i.e., batch optimization) approach. The methods are tested on synthetic data as well as on empirical data collected directly from a 2019 model year ACC vehicle using data from sensors that are part of the stock ACC system. The online methods are scalable and provide comparable accuracy to the batch method. RLS runs in real time and is two orders of magnitude faster than the batch method for modest sized (e.g., 15 min) datasets. The particle filter also runs in real- time, and is also suitable in streaming applications in which the datasets can grow arbitrarily large.
Wang, Yanbing, Gunter, George, Nice, Matthew, Monache, Maria Delle, & Work, Daniel B. (2020). Online Parameter Estimation Methods for Adaptive Cruise Control Systems. IEEE Transactions on Intelligent Vehicles, 6(2). https://doi.org/10.1109/tiv.2020.3023674
@article{osti_1779049,
author = {Wang, Yanbing and Gunter, George and Nice, Matthew and Monache, Maria Delle and Work, Daniel B.},
title = {Online Parameter Estimation Methods for Adaptive Cruise Control Systems},
annote = {Modeling Adaptive Cruise Control (ACC) vehicles enables the understanding of the impact of these vehicles on traffic flow. In this work, two online methods are used to provide real time system identification of ACC enabled vehicles. The first technique is a recursive least squares (RLS) approach, while the second method solves a nonlinear joint state and parameter estimation problem via particle filtering (PF). We provide a parameter identifiability analysis for both methods to analytically show that the model parameters are not identifiable using equilibrium driving. The accuracy and computational runtime of the online methods are compared to a commonly used offline simulation-based optimization (i.e., batch optimization) approach. The methods are tested on synthetic data as well as on empirical data collected directly from a 2019 model year ACC vehicle using data from sensors that are part of the stock ACC system. The online methods are scalable and provide comparable accuracy to the batch method. RLS runs in real time and is two orders of magnitude faster than the batch method for modest sized (e.g., 15 min) datasets. The particle filter also runs in real- time, and is also suitable in streaming applications in which the datasets can grow arbitrarily large.},
doi = {10.1109/tiv.2020.3023674},
url = {https://www.osti.gov/biblio/1779049},
journal = {IEEE Transactions on Intelligent Vehicles},
issn = {ISSN 2379-8858},
number = {2},
volume = {6},
place = {United States},
publisher = {IEEE},
year = {2020},
month = {09}}
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 368, Issue 1928https://doi.org/10.1098/rsta.2010.0189