We introduce WarNav, a novel real-world dataset constructed from images of the open-source DATTALION repository, specifically tailored to enable the development and benchmarking of semantic segmentation models for autonomous ground vehicle navigation in unstructured, conflict-affected environments. This dataset addresses a critical gap between conventional urban driving resources and the unique operational scenarios encountered by unmanned systems in hazardous and damaged war-zones. We detail the methodological challenges encountered, ranging from data heterogeneity to ethical considerations, providing guidance for future efforts that target extreme operational contexts. To establish performance references, we report baseline results on WarNav using several state-of-the-art semantic segmentation models trained on structured urban scenes. We further analyse the impact of training data environments and propose a first step towards effective navigability in challenging environments with the constraint of having no annotation of the targeted images. Our goal is to foster impactful research that enhances the robustness and safety of autonomous vehicles in high-risk scenarios while being frugal in annotated data.

翻译：本文介绍了WarNav，一个基于开源DATTALION图像库构建的新型真实世界数据集，专门用于支持非结构化、受冲突影响环境下自主地面车辆导航的语义分割模型开发与性能评估。该数据集填补了传统城市驾驶资源与危险损毁战区内无人系统所面临独特任务场景之间的关键空白。我们详细阐述了从数据异质性到伦理考量等方法学挑战，为未来针对极端作业环境的研究提供指导。为建立性能参考基准，我们报告了在结构化城市场景下训练的多种先进语义分割模型在WarNav数据集上的基线结果。进一步分析了训练数据环境的影响，并在目标图像无标注的约束条件下，提出了实现挑战性环境中有效可通行性分析的初步方案。本研究旨在推动能够提升高风险场景中自动驾驶系统鲁棒性与安全性的重要研究，同时降低对标注数据的依赖。