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arXiv · 2508.05773

GPU-Accelerated Barrier-Rate Guided MPPI Control for Tractor-Trailer Systems

Keyvan Majd·Hardik Parwana·Bardh Hoxha·Steven Hong·Hideki Okamoto·Georgios Fainekos
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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.

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Keyvan Majd, Hardik Parwana, Bardh Hoxha, Steven Hong, Hideki Okamoto, Georgios Fainekos. 2025-08-07. GPU-Accelerated Barrier-Rate Guided MPPI Control for Tractor-Trailer Systems. https://doi.org/10.1109/itsc60802.2025.11423534

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