@inproceedings{ward_tractor-trailer_2025,
	title = {Tractor-{Trailer} {Vehicle} {Rollover} {Avoidance} {Using} {Chance}-{Constrained} {Reference} {Governor} and {Data}-{Driven} {Ultra}-{Local} {Model}},
	url = {https://www.sciencedirect.com/science/article/pii/S2405896325001314?via%3Dihub},
	abstract = {This work presents a novel method for preventing tractor-trailer rollovers through the use of a chance-constrained reference governor and a data-driven ultra-local model. Typical commercial rollover prevention systems function by adding a system that interfaces with the trailer brakes and is capable of measuring the lateral acceleration of the trailer. If certain lateral acceleration thresholds are exceeded the trailer brakes will activate to reduce the speed of the tractor-trailer system. The system proposed in this work assumes that the truck is semi-autonomous and following a path generated by some high-level path planner. Rollover avoidance is then achieved by minimally modifying the reference path to ensure that a prescribed lateral acceleration threshold is not exceeded through the use of a reference governor. Because, in a realistic system, stochastic disturbances such as estimation errors or wind disturbances exist and since an analytical model of the truck is likely unknown, a data-driven model is leveraged along with a chance-constrained formulation of the reference governor. This chance-constrained reference governor is demonstrated to successfully constrain the tractor-trailer lateral acceleration below a prescribed threshold with a probability β.},
	language = {en},
	author = {Ward, Jacob and Li, Nan and Bevly, David and Brown, Lowell},
	month = jan,
	year = {2025},
}

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