Efficient optimization-based trajectory planning for truck-trailer systems

Abstract

This paper tackles the complex problem of trajectory planning for trucks with multiple trailers, with a specific focus on autonomous parking assistance applications. These systems aim to autonomously guide vehicles from a starting position to a target location while effectively navigating real-world obstacles. We propose a novel six-phase approach that combines global and local optimization techniques, enabling the efficient and accurate generation of reference trajectories. Our method is validated in a case study involving a truck with two trailers, illustrating its capability to handle intricate parking scenarios requiring precise obstacle avoidance and high maneuverability. Results demonstrate that the proposed strategy significantly improves trajectory planning efficiency and robustness in challenging environments.