WarNav: An Autonomous Driving Benchmark for Segmentation of Navigable Zones in War Scenes

TL;DR

WarNav introduces a new dataset for semantic segmentation of navigable zones in war scenes, aiming to improve autonomous vehicle navigation in hazardous, unstructured environments with minimal annotation.

Contribution

The paper presents WarNav, a novel dataset tailored for autonomous navigation in war zones, along with baseline results and analysis of training data impact in extreme scenarios.

Findings

Baseline models perform variably on war scene data.

Training data environment significantly affects model performance.

Effective navigability can be approached with minimal annotations.

Abstract

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.