发表机构
University of California, Irvine; TIER IV, Inc.（加州大学尔湾分校; TIER IV 公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文分析了将 Autoware 自动驾驶系统从公共道路扩展到建筑工地大型自卸卡车时，在感知、建图、定位、规划和控制等模块的差距，重点介绍了基于 LiDAR 的感知方案与现场测试发现，并提出了端到端自动驾驶路线图。
AI 中文摘要
Autoware 是一个开源自动驾驶软件平台，被研究人员和行业开发者广泛采用。它最初主要面向公共道路应用开发，包括乘用车、出租车和公交车，如今正越来越多地扩展到建筑工地和农业场地等非公路环境。然而，建筑工地在根本上不同于公共道路，对传统自动驾驶系统的许多假设提出了挑战。建筑工地以非结构化地形、空气中扬尘、持续变化的场地条件以及建筑专用物体为特征。本文介绍了将基于 Autoware 的自动驾驶系统扩展到在建筑工地运行的大型自卸卡车的持续努力中获得的经验教训。我们识别了 Autoware 技术栈中感知、建图、定位、感知、规划和控制模块在公共道路与非公路建筑应用之间的差距。随后，我们讨论了解决这些差距的潜在方案，特别强调了正在进行的基于 LiDAR 的感知流程开发以及现场测试的发现。最后，我们提出了面向建筑车辆端到端自动驾驶的路线图。
英文摘要
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.
Comments2026 IROS 5th Workshop on Future of Construction, 4 pages, 5 figures
