Majd, Keyvan (Toyota Research Institute NA), Parwana, Hardik (University of Michigan), Hoxha, Bardh (Toyota Motor North America), Hong, Steven (Toyota Motor North America R&D), Okamoto, Hideki (Toyota Motor North America), Fainekos, Georgios (Toyota Motor North America)

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

Scheduled for presentation during the Regular Session "S41b-Motion Planning, Trajectory Optimization, and Control for Autonomous Vehicles" (FR-EA-T41), Friday, November 21, 2025, 14:50−14:50, Broadbeach 1&2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

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 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.