センサ融合と動作計画を用いたトラクタートレーラー輸送車両の駐車支援
Parking Assistance for Trailer-Truck Transport Vehicles Using Sensor Fusion and Motion Planning
トラクタートレーラーの自動駐車を実現するため、センサ融合、Hybrid A*経路計画、非線形モデル予測制御を統合したフレームワークを提案し、オープンソースのA*シミュレーションを拡張して実証した。
著者: George Alenchery, Thomas Jeske, Tova Quinones, Lentz Fortune, Tristan Lindo-Slones, Amber Jones, Jordan Fletcher
分類: cs.RO
原文アブストラクト
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.