GPU-Accelerated Barrier-Rate Guided MPPI Control for Tractor-Trailer Systems
Abstract
Articulated vehicles such as tractor-trailers, yard trucks, and similar platforms must often reverse and maneuver in cluttered spaces where pedestrians are present. We present how Barrier-Rate guided Model Predictive Path Integral (BR-MPPI) control can solve navigation in such challenging environments. BR-MPPI embeds Control Barrier Function (CBF) constraints directly into the path-integral update. By steering the importance-sampling distribution toward collision-free, dynamically feasible trajectories, BR-MPPI enhances the exploration strength of MPPI and improves robustness of resulting trajectories. The method is evaluated in the high-fidelity CarMaker simulator on a 12 [m] tractor-trailer tasked with reverse and forward parking in a parking lot. BR-MPPI computes control inputs in above 100 [Hz] on a single GPU (for scenarios with eight obstacles) and maintains better parking clearance than a standard MPPI baseline and an MPPI with collision cost baseline.
Cite
@article{arxiv.2508.05773,
title = {GPU-Accelerated Barrier-Rate Guided MPPI Control for Tractor-Trailer Systems},
author = {Keyvan Majd and Hardik Parwana and Bardh Hoxha and Steven Hong and Hideki Okamoto and Georgios Fainekos},
journal= {arXiv preprint arXiv:2508.05773},
year = {2026}
}
Comments
Accepted to IEEE ITSC 2025