String instability mitigation of adaptive cruise control without modifying control laws: trajectory shaper and parameter estimation

Zhou, Anye; Zhou, Hao; Laval, Jorge; Peeta, Srinivas

doi:10.1080/21680566.2025.2473885

String instability mitigation of adaptive cruise control without modifying control laws: trajectory shaper and parameter estimation

Journal Article · Wed Apr 09 00:00:00 EDT 2025 · Transportmetrica. B, Transport Dynamics

DOI:https://doi.org/10.1080/21680566.2025.2473885· OSTI ID:2573375

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Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Univ. of South Florida, Tampa, FL (United States)
Georgia Institute of Technology, Atlanta, GA (United States)

Vehicle automation technologies equip vehicles with adaptive cruise control (ACC) systems, which relieve driving fatigue. However, recent studies have shown that the current ACC systems are string-unstable (i.e., exacerbate traffic congestion). To achieve string stability, most existing studies directly modify the control algorithms of ACC systems. Alternatively, this study proposes a trajectory shaper (TS)-based method, which only modifies the trajectory information of the predecessor vehicle, so that the ego vehicle driven by a string-unstable ACC system leverages the modified trajectory information to achieve string stability. To devise the TS-based method, an offline-online parameter estimation method integrating batch optimization and an extended Kalman filter is applied to estimate the parameters of an ACC system. The proposed TS-based method is cost-effective during implementation, as it avoids modifying existing ACC control algorithms (which entails a complex analysis of control systems and parameter tuning). In conclusion, the effectiveness of the proposed TS-based method is validated through extensive numerical experiments.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; National Science Foundation (NSF)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 2573375

Journal Information:: Transportmetrica. B, Transport Dynamics, Journal Name: Transportmetrica. B, Transport Dynamics Journal Issue: 1 Vol. 13; ISSN 2168-0566; ISSN 2168-0582

Publisher:: Taylor & FrancisCopyright Statement

Country of Publication:: United States

Language:: English

References (23)

System Identification Ljung, Lnnart Wiley Encyclopedia of Electrical and Electronics Engineering https://doi.org/10.1002/047134608X.W1046	book	December 1999
Optimal velocity model for traffic flow Sugiyama, Yūki Computer Physics Communications, Vol. 121-122 https://doi.org/10.1016/S0010-4655(99)00366-5	journal	September 1999
String stability for vehicular platoon control: Definitions and analysis methods Feng, Shuo; Zhang, Yi; Li, Shengbo Eben Annual Reviews in Control, Vol. 47 https://doi.org/10.1016/j.arcontrol.2019.03.001	journal	January 2019
Trajectory Shaper: A Solution for Disrupted Cooperative Adaptive Cruise Control Zhou, Anye; Wang, Zejiang; Cook, Adian IFAC-PapersOnLine, Vol. 58, Issue 28 https://doi.org/10.1016/j.ifacol.2025.01.084	journal	January 2024
Identifiability of car-following dynamics Wang, Yanbing; Monache, Maria Laura Delle; Work, Daniel B. Physica D: Nonlinear Phenomena, Vol. 430 https://doi.org/10.1016/j.physd.2021.133090	journal	February 2022
Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns Montanino, Marcello; Punzo, Vincenzo Transportation Research Part B: Methodological, Vol. 80 https://doi.org/10.1016/j.trb.2015.06.010	journal	October 2015
A real-time deployable model predictive control-based cooperative platooning approach for connected and autonomous vehicles Wang, Jian; Gong, Siyuan; Peeta, Srinivas Transportation Research Part B: Methodological, Vol. 128 https://doi.org/10.1016/j.trb.2019.08.002	journal	October 2019
OpenACC. An open database of car-following experiments to study the properties of commercial ACC systems Makridis, Michail; Mattas, Konstantinos; Anesiadou, Aikaterini Transportation Research Part C: Emerging Technologies, Vol. 125 https://doi.org/10.1016/j.trc.2021.103047	journal	April 2021
Significance of low-level control to string stability under adaptive cruise control: Algorithms, theory and experiments Zhou, Hao; Zhou, Anye; Li, Tienan Transportation Research Part C: Emerging Technologies, Vol. 140 https://doi.org/10.1016/j.trc.2022.103697	journal	July 2022
Implications of stop-and-go traffic on training learning-based car-following control Zhou, Anye; Peeta, Srinivas; Zhou, Hao Transportation Research Part C: Emerging Technologies, Vol. 168 https://doi.org/10.1016/j.trc.2024.104578	journal	November 2024
Stability and capacity for heterogeneous traffic flow mixed with vehicles in multiple controls Yu, Weijie; Hua, Xuedong; Wang, Wei Transportmetrica B: Transport Dynamics, Vol. 11, Issue 1 https://doi.org/10.1080/21680566.2022.2113476	journal	September 2022
A cooperative merging speed control strategy of CAVs based on virtual platoon in on-ramp merging system Yang, Wenzhang; Dong, Changyin; Wang, Hao Transportmetrica B: Transport Dynamics, Vol. 11, Issue 1 https://doi.org/10.1080/21680566.2023.2217704	journal	May 2023
Nonlinear longitudinal cooperative control of heterogeneous connected vehicle platoon considering car-following interactions and communication delay Li, Yongfu; Zhang, Sen; Huang, Longwang Transportmetrica B: Transport Dynamics, Vol. 11, Issue 1 https://doi.org/10.1080/21680566.2023.2241643	journal	August 2023
Unlocking potential capacity benefits of electric vehicles (EVs) with adaptive cruise control (ACC) Lapardhaja, Servet; Yagantekin, Kemal Ulas; Yang, Mingyuan Transportmetrica B: Transport Dynamics, Vol. 11, Issue 1 https://doi.org/10.1080/21680566.2023.2271668	journal	October 2023
Joint optimisation of vehicle trajectory and signal control at intersections mixed with connected automated vehicles: a departure sequence estimation-based approach Chen, Peng; Wei, Lei; Wang, Tong Transportmetrica B: Transport Dynamics, Vol. 12, Issue 1 https://doi.org/10.1080/21680566.2024.2303055	journal	January 2024
Congested traffic states in empirical observations and microscopic simulations Treiber, Martin; Hennecke, Ansgar; Helbing, Dirk Physical Review E, Vol. 62, Issue 2 https://doi.org/10.1103/PhysRevE.62.1805	journal	August 2000
Zero time delay input shaping for smooth settling of industrial robots No authors listed 2016 IEEE International Conference on Automation Science and Engineering (CASE) https://doi.org/10.1109/COASE.2016.7743459	conference	August 2016
Input-Dependent Structural Identifiability of Nonlinear Systems Villaverde, Alejandro F.; Evans, Neil D.; Chappell, Michael J. IEEE Control Systems Letters, Vol. 3, Issue 2 https://doi.org/10.1109/LCSYS.2018.2868608	journal	April 2019
String-Stable CACC Design and Experimental Validation: A Frequency-Domain Approach Naus, Gerrit J. L.; Vugts, Rene P. A.; Ploeg, Jeroen IEEE Transactions on Vehicular Technology, Vol. 59, Issue 9 https://doi.org/10.1109/TVT.2010.2076320	journal	November 2010
Cooperative Adaptive Cruise Control for a Platoon of Connected and Autonomous Vehicles considering Dynamic Information Flow Topology Gong, Siyuan; Zhou, Anye; Peeta, Srinivas Transportation Research Record: Journal of the Transportation Research Board, Vol. 2673, Issue 10 https://doi.org/10.1177/0361198119847473	journal	May 2019
Smooth-Switching Control-Based Cooperative Adaptive Cruise Control by Considering Dynamic Information Flow Topology Zhou, Anye; Gong, Siyuan; Wang, Chaojie Transportation Research Record: Journal of the Transportation Research Board, Vol. 2674, Issue 4 https://doi.org/10.1177/0361198120910734	journal	March 2020
Calibrating Car-Following Models by Using Trajectory Data: Methodological Study Kesting, Arne; Treiber, Martin Transportation Research Record: Journal of the Transportation Research Board, Vol. 2088, Issue 1 https://doi.org/10.3141/2088-16	journal	January 2008
Method for Estimating Time to Collision at Braking in Real-World, Lead Vehicle Stopped Rear-End Crashes for Use in Pre-Crash System Design Kusano, Kristofer D.; Gabler, Hampton SAE International Journal of Passenger Cars - Mechanical Systems, Vol. 04, Issue 1 https://doi.org/10.4271/2011-01-0576	journal	April 2011

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Adaptive cruise control
parameter estimation
stop-and-go traffic
string stability
trajectory shaper

String instability mitigation of adaptive cruise control without modifying control laws: trajectory shaper and parameter estimation

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References (23)

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