Loading [MathJax]/extensions/MathMenu.js
Stochastic Calibration of Automated Vehicle Car-Following Control: An Approximate Bayesian Computation Approach | IEEE Journals & Magazine | IEEE Xplore

Stochastic Calibration of Automated Vehicle Car-Following Control: An Approximate Bayesian Computation Approach


Abstract:

This paper presents a stochastic calibration method based on Approximate Bayesian Computation (ABC). This method is applied to calibrate two car-following control models:...Show More

Abstract:

This paper presents a stochastic calibration method based on Approximate Bayesian Computation (ABC). This method is applied to calibrate two car-following control models: linear control and model predictive control (MPC). The method is likelihood-function-free, where the likelihood function is replaced by simulation to approximate the posterior distribution of model parameters. This structure affords flexibility to calibrate posterior joint distributions of complex models, even those without analytical closed forms such as MPC. Two experiments were conducted to evaluate how well the proposed method reproduces: (i) marginal and joint distributions of model parameters, using synthetic data and (ii) vehicle trajectories (acceleration, speed, and position), using field data involving two commercial adaptive cruise control (ACC) systems. The results showed that the ABC method can reproduce marginal and joint distributions reasonably well for the linear controller as well as the non-analytical MPC-based controller, which was previously infeasible. The method can also robustly characterize the commercial ACC behavior at the trajectory level, which suggests that the simple linear controller better describes their behavior.
Published in: IEEE Transactions on Intelligent Transportation Systems ( Volume: 26, Issue: 3, March 2025)
Page(s): 3115 - 3127
Date of Publication: 16 January 2025

ISSN Information:

Funding Agency:

References is not available for this document.

I. Introduction

Automated vehicles (AVs) are garnering attention for their potential to transform transportation systems. Among many potential benefits, AVs can presumably improve traffic efficiency, stability, and safety through advanced sensing and communication [1], [2]. AV car-following (CF) control, known as adaptive cruise control (ACC) is one of the earliest and most well-known automation features available in the market, enabling level 1 automation as defined by the Society of Automotive Engineers (SAE) [3]. ACC algorithms have been shown to behave differently from human driving behaviors. This has sparked a great deal of interest to better understand the behavior of vehicles with ACC and its traffic impacts through field experiments [4], [5], [6].

Select All
1.
D. Chen, S. Ahn, M. Chitturi and D. A. Noyce, "Towards vehicle automation: Roadway capacity formulation for traffic mixed with regular and automated vehicles", Transp. Res. B Methodol., vol. 100, pp. 196-221, Jun. 2017.
2.
Y. Zhou, S. Ahn, M. Chitturi and D. A. Noyce, "Rolling horizon stochastic optimal control strategy for ACC and CACC under uncertainty", Transp. Res. C Emerg. Technol., vol. 83, pp. 61-76, Oct. 2017.
3.
S. E. Shladover, C. Nowakowski, X.-Y. Lu and R. Ferlis, "Cooperative adaptive cruise control: Definitions and operating concepts", Transp. Res. Rec. J. Transp. Res. Board, vol. 2489, no. 1, pp. 145-152, Jan. 2015.
4.
T. Li, D. Chen, H. Zhou, Y. Xie and J. Laval, "Fundamental diagrams of commercial adaptive cruise control: Worldwide experimental evidence", Transp. Res. C Emerg. Technol., vol. 134, Jan. 2022.
5.
M. Makridis, K. Mattas and B. Ciuffo, "Response time and time headway of an adaptive cruise Control. An empirical characterization and potential impacts on road capacity", IEEE Trans. Intell. Transp. Syst., vol. 21, no. 4, pp. 1677-1686, Apr. 2020.
6.
M. Makridis, K. Mattas, A. Anesiadou and B. Ciuffo, "OpenACC. An open database of car-following experiments to study the properties of commercial ACC systems", Transp. Res. C Emerg. Technol., vol. 125, Apr. 2021.
7.
F. Morbidi, P. Colaneri and T. Stanger, "Decentralized optimal control of a car platoon with guaranteed string stability", Proc. Eur. Control Conf. (ECC), pp. 3494-3499, Jul. 2013.
8.
Y. He, M. Montanino, K. Mattas, V. Punzo and B. Ciuffo, "Physics-augmented models to simulate commercial adaptive cruise control (ACC) systems", Transp. Res. C Emerg. Technol., vol. 139, Jun. 2022.
9.
S. Gong and L. Du, "Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles", Transp. Res. B Methodol., vol. 116, pp. 25-61, Oct. 2018.
10.
Y. Zhou, M. Wang and S. Ahn, "Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability", Transp. Res. B Methodol., vol. 128, pp. 69-86, Oct. 2019.
11.
H. Shi, Y. Zhou, K. Wu, X. Wang, Y. Lin and B. Ran, "Partially connected automated vehicle cooperative control strategy with a deep reinforcement learning approach", arXiv:2012.01841, 2020.
12.
X. Qu, Y. Yu, M. Zhou, C.-T. Lin and X. Wang, "Jointly dampening traffic oscillations and improving energy consumption with electric connected and automated vehicles: A reinforcement learning based approach", Appl. Energy, vol. 257, Jan. 2020.
13.
D. Moser, R. Schmied, H. Waschl and L. del Re, "Flexible spacing adaptive cruise control using stochastic model predictive control", IEEE Trans. Control Syst. Technol., vol. 26, no. 1, pp. 114-127, Jan. 2018.
14.
M. Wang, W. Daamen, S. P. Hoogendoorn and B. van Arem, "Rolling horizon control framework for driver assistance systems. Part I: Mathematical formulation and non-cooperative systems", Transp. Res. C Emerg. Technol., vol. 40, pp. 271-289, Mar. 2014.
15.
Y. Zhou, S. Ahn, M. Wang and S. Hoogendoorn, "Stabilizing mixed vehicular platoons with connected automated vehicles: An H-infinity approach", Transp. Res. B Methodol., vol. 132, pp. 152-170, Feb. 2020.
16.
N. Chiabaut, L. Leclercq and C. Buisson, "From heterogeneous drivers to macroscopic patterns in congestion", Transp. Res. B Methodol., vol. 44, no. 2, pp. 299-308, Feb. 2010.
17.
V. Punzo and M. Montanino, "Speed or spacing? Cumulative variables and convolution of model errors and time in traffic flow models validation and calibration", Transp. Res. B Methodol., vol. 91, pp. 21-33, Sep. 2016.
18.
M. Treiber, A. Kesting and D. Helbing, "Three-phase traffic theory and two-phase models with a fundamental diagram in the light of empirical stylized facts", Transp. Res. B Methodol., vol. 44, no. 8, pp. 983-1000, Sep. 2010.
19.
G. F. Newell, "A simplified car-following theory: A lower order model", Transp. Res. B Methodol., vol. 36, no. 3, pp. 195-205, Mar. 2002.
20.
F. de Souza and R. Stern, "Calibrating microscopic car-following models for adaptive cruise control vehicles: Multiobjective approach", J. Transp. Eng. A Syst., vol. 147, no. 1, Jan. 2021.
21.
G. Gunter et al., "Are commercially implemented adaptive cruise control systems string stable?", IEEE Trans. Intell. Transp. Syst., vol. 22, no. 11, pp. 6992-7003, Nov. 2021.
22.
S. Hoogendoorn, R. Hoogendoorn, M. Wang and W. Daamen, "Modeling driver driver support and cooperative systems with dynamic optimal control", Transp. Res. Rec. J. Transp. Res. Board, vol. 2316, no. 1, pp. 20-30, Jan. 2012.
23.
M. Rahman, M. Chowdhury, T. Khan and P. Bhavsar, "Improving the efficacy of car-following models with a new stochastic parameter estimation and calibration method", IEEE Trans. Intell. Transp. Syst., vol. 16, no. 5, pp. 2687-2699, Oct. 2015.
24.
D. Sha, K. Ozbay and Y. Ding, "Applying Bayesian optimization for calibration of transportation simulation models", Transp. Res. Rec., vol. 2674, no. 10, pp. 215-228, Aug. 2020.
25.
M. Rafati Fard and A. Shariat Mohaymany, "A copula-based estimation of distribution algorithm for calibration of microscopic traffic models", Transp. Res. C Emerg. Technol., vol. 98, pp. 449-470, Jan. 2019.
26.
M. Saifuzzaman and Z. Zheng, "Incorporating human-factors in car-following models: A review of recent developments and research needs", Transp. Res. C Emerg. Technol., vol. 48, pp. 379-403, Nov. 2014.
27.
K. I. Ahmed, "Modeling drivers’ acceleration and lane changing behavior", 1999.
28.
S. Hoogendoorn and R. Hoogendoorn, "Calibration of microscopic traffic-flow models using multiple data sources", Philos. Trans. Roy. Soc. A Math. Phys. Eng. Sci., vol. 368, no. 1928, pp. 4497-4517, Oct. 2010.
29.
S. P. Hoogendoorn and R. Hoogendoorn, "Generic calibration framework for joint estimation of car-following models by using microscopic data", Transp. Res. Rec. J. Transp. Res. Board, vol. 2188, no. 1, pp. 37-45, Jan. 2010.
30.
C. P. I. J. van Hinsbergen, W. J. Schakel, V. L. Knoop, J. W. C. van Lint and S. P. Hoogendoorn, "A general framework for calibrating and comparing car-following models", Transportmetrica A Transp. Sci., vol. 11, no. 5, pp. 420-440, May 2015.

Contact IEEE to Subscribe

References

References is not available for this document.