ITSC 2025 Paper Abstract

Paper WE-EA-T11.5

Aertssen, Aron (Eindhoven University of Technology), Huisman, Rudolf (DAF), Besselink, Igo (Eindhoven University of Technology), Elfring, Jos (Eindhoven University of Technology), v.d. Molengraft, M.J.G. (Eindhoven University of Technology)

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

Scheduled for presentation during the Regular Session "S11b-Motion Planning, Trajectory Optimization, and Control for Autonomous Vehicles" (WE-EA-T11), Wednesday, November 19, 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 19, 2025

Keywords Real-time Motion Planning and Control for Autonomous Vehicles in ITS Networks, Autonomous Freight Transport Systems and Fleet Management Solutions

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.