Robust V2X Cruise Control for Class 8 Trucks in the Presence of Traffic Lights

<div class="section abstract"><div class="htmlview paragraph">An implementation of a robust predictive cruise control method for class 8 trucks utilizing V2X communication with connected traffic lights is presented in this work. This method accounts for traffic signal phases with the goal of reducing energy consumption when possible while respecting safety concerns. Tightened constraints are created using a robust model predictive control (RMPC) framework in which constraints are modified so that the safety critical requirements are satisfied even in the presence of disturbances, while requiring only the expected bounds of the disturbances to be provided. In particular, variation in the actuator performance under different conditions presents a unique challenge for this application, which the approach applied in this work is well-suited to handle. The errors resulting from lower-level control and actuator performance are accounted for by treating them as bounded and additive disturbances on the states of the model used in the higher level MPC, and the RMPC method is demonstrated to satisfy constraints in the presence of arbitrary bounded disturbances that can be modeled in this way. Simulation results show that these tightened constraints successfully account for error due to low-level control and actuator performance for class 8 trucks. Furthermore, tests were performed on hardware which show the capability for real-time application.</div></div>

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Publication Details

Journal
SAE technical papers on CD-ROM/SAE technical paper series
Published
2025-04-01
DOI
https://doi.org/10.4271/2025-01-8033
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
0.00
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article

Robust V2X Cruise Control for Class 8 Trucks in the Presence of Traffic Lights

E. Christopher Ellison, David M. Bevly, Jacob Ward, Lowell Brown
SAE technical papers on CD-ROM/SAE technical paper series
Traffic control and management
article

Robust V2X Cruise Control for Class 8 Trucks in the Presence of Traffic Lights

E. Christopher Ellison, David M. Bevly, Jacob Ward, Lowell Brown
article en

Abstract

<div class="section abstract"><div class="htmlview paragraph">An implementation of a robust predictive cruise control method for class 8 trucks utilizing V2X communication with connected traffic lights is presented in this work. This method accounts for traffic signal phases with the goal of reducing energy consumption when possible while respecting safety concerns. Tightened constraints are created using a robust model predictive control (RMPC) framework in which constraints are modified so that the safety critical requirements are satisfied even in the presence of disturbances, while requiring only the expected bounds of the disturbances to be provided. In particular, variation in the actuator performance under different conditions presents a unique challenge for this application, which the approach applied in this work is well-suited to handle. The errors resulting from lower-level control and actuator performance are accounted for by treating them as bounded and additive disturbances on the states of the model used in the higher level MPC, and the RMPC method is demonstrated to satisfy constraints in the presence of arbitrary bounded disturbances that can be modeled in this way. Simulation results show that these tightened constraints successfully account for error due to low-level control and actuator performance for class 8 trucks. Furthermore, tests were performed on hardware which show the capability for real-time application.</div></div>

SAE technical papers on CD-ROM/SAE technical paper seriesVol. 1
Daimler (United Kingdom) (GB), Daimler Truck (United States), Auburn University (US)
Openalex Percentile: Top 92%
Traffic control and management
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Robust V2X Cruise Control for Class 8 Trucks in the Presence of Traffic Lights — E. Christopher Ellison, David M. Bevly, et al. · SAE technical papers on CD-ROM/SAE technical paper series (2025) | TGRS Research Map | TGRS