🤖 AI Summary
This study addresses the challenge that unstructured construction site environments violate the foundational assumptions of conventional on-road autonomous driving systems, rendering existing frameworks inapplicable to engineering vehicles. Building upon the open-source Autoware platform, this work systematically analyzes adaptation gaps within the perception, localization, and planning modules for autonomous dump trucks, proposing an end-to-end autonomy roadmap tailored to dynamic construction environments. The research primarily reconstructs the LiDAR point cloud perception pipeline and conducts multi-module co-optimization alongside field testing. By delineating improvement pathways for each core module and validating the robustness of LiDAR perception under complex operating conditions, this study effectively bridges the technological divide between on-road and off-road scenarios, establishing both theoretical and engineering foundations for the automation of heavy-duty construction vehicles.
📝 Abstract
Autoware is an open-source autonomous driving software platform widely adopted by researchers and industry developers. Originally developed primarily for public-road applications, including passenger vehicles, taxis, and buses, Autoware is increasingly being extended to off-road environments such as construction and agricultural sites. Construction sites, however, differ fundamentally from public roads and challenge many assumptions underlying conventional autonomous driving systems. They are characterized by unstructured terrain, airborne dust, continuously evolving site conditions, and construction-specific objects. This paper presents lessons learned from ongoing efforts to extend an Autoware-based autonomous driving system to large dump trucks operating at construction sites. We identify gaps between public-road and off-road construction applications across the sensing, mapping, localization, perception, planning, and control modules of the Autoware stack. We then discuss potential solutions for addressing these gaps, with particular emphasis on ongoing LiDAR-based perception pipeline development and findings from field testing. Finally, we propose a roadmap toward end-to-end autonomous driving for construction vehicles.