Trajectory Planning for an Articulated Commercial Vehicle using Model Predictive Contouring Control

TL;DR

This paper extends Model Predictive Contouring Control to articulated commercial vehicles, enabling scenario-dependent prioritization and boundary constraints, with successful simulation and initial real-world tests.

Contribution

It introduces a novel MPCC-based trajectory planning method tailored for tractor-semitrailers, addressing size, maneuverability, and scenario-specific requirements.

Findings

Successful simulation of logistic scenarios in forward and reverse

Effective scenario-dependent weighting of vehicle anchor points

Initial tests demonstrate practical applicability on a prototype vehicle

Abstract

This paper presents a trajectory planning method for articulated commercial vehicles, specifically tractor-semitrailers, based on Model Predictive Contouring Control (MPCC). Although MPCC has proven effective for passenger cars, it is generally ill-suited for tractor-semitrailers. These vehicles are significantly larger, the semitrailer follows a different path than the tractor, and reversing maneuvers are unstable and prone to jackknifing. Furthermore, practical driving scenarios often require scenario-dependent prioritization of different vehicle `anchor points', e.g., prioritizing the semitrailer position during docking or the tractor position when parking to charge. Therefore, we extend MPCC to enable scenario-dependent weighting of these anchor points and incorporate explicit road-boundary constraints for the front and rear tractor axles and the semitrailer axle, thereby ensuring that all considered wheels remain within the drivable area. The simulation results demonstrate the successful navigation of a representative logistic scenario in both forward and reverse direction. Furthermore, the influence of the optimization parameters on the trajectories is analyzed, providing insights into controlling the vehicle behavior. Finally, first tests using a full-scale prototype vehicle show the practical applicability of the approach.