Parking Assistance for Trailer-Truck Transport Vehicles Using Sensor Fusion and Motion Planning

TL;DR

This paper proposes an integrated autonomous truck parking framework using sensor fusion, hybrid A* path planning, NMPC, and data-driven systems, demonstrated through a simulation with articulated vehicle path planning.

Contribution

It introduces a system-level approach combining perception, motion planning, and control for autonomous trailer-truck parking, including a simulation adaptation of open-source A* planning.

Findings

Successful simulation of articulated vehicle path planning

Integration of sensor fusion with Hybrid A* and NMPC demonstrated

Jackknife prevention identified as future work

Abstract

Autonomous driving technology has rapidly evolved over the past decade, offering significant improvements in transportation efficiency, safety, and cost reduction. While much of the progress has focused on highway driving and obstacle avoidance, low-speed maneuvers such as parking remain among the most difficult challenges for autonomous systems. This challenge is especially pronounced in trailer-truck transport vehicles due to their articulated motion and environmental constraints. This paper presents a proposed framework for autonomous truck parking that integrates perception, motion planning, control systems, and infrastructure awareness. By combining sensor fusion, Hybrid A* path planning, nonlinear model predictive control (NMPC), and data-driven parking systems, this work highlights the importance of system-level coordination for reliable and scalable autonomous parking solutions. As a proof-of-concept implementation, we adapted an open-source A* path planning simulation to incorporate a tractor-trailer kinematic model, demonstrating articulated vehicle path planning within a command-line simulation environment, with jackknife prevention identified as an area requiring further development.