UnsDrive: Towards Robust End-to-End Autonomous Driving in Unstructured Scenes
2026-08-10 • Robotics
Robotics
AI summaryⓘ
The authors developed UnsDrive, a planning system specifically for self-driving vehicles in complex mining areas where roads are unclear and visibility is limited. Their system maps out known, free, and unknown spaces to help plan safer routes, even in places the vehicle hasn’t fully seen yet. They also created MineLoop, a special simulator that tests how well vehicles drive under mining conditions. Tests showed that UnsDrive plans better routes and avoids crashes more effectively than other methods in these tough environments.
end-to-end planningautonomous drivingunstructured environmentsoccupancy representationmulti-frame visibilitytrajectory planningpartial observabilitysimulationmining environments
Authors
Nanxin Zeng, Ruiqi Song, Xiangyu Guo, Baiyong Ding, Yunfeng Ai
Abstract
End-to-end planning has shown strong promise for autonomous driving, but most existing methods are designed for structured urban roads and generalize poorly to unstructured mining environments. In such settings, weak road structure, terrain-induced occlusions, degraded visibility, and large unobserved regions make safe planning particularly challenging. To address these challenges, we propose UnsDrive, an end-to-end planner designed for unstructured mining scenes. UnsDrive builds an unknown-aware occupancy representation that explicitly models occupied, free, and unknown space using multi-frame visibility cues, and conditions a flow-matching planner on this representation to generate multimodal future trajectories. To improve safety under partial observability, we further introduce an occupancy trajectory consistency loss and an uncertainty-aware trajectory scorer that penalize trajectories entering non-traversable or unobserved regions. We also present MineLoop, a mining-oriented closed-loop simulator for evaluating autonomous driving under irregular road geometry, degraded visibility, heavy-vehicle interactions, and mining-specific operational constraints. Experiments in both open-loop and closed-loop settings show that UnsDrive consistently outperforms strong baselines in trajectory accuracy, collision avoidance, and long-horizon driving robustness. These results demonstrate the value of explicit unknown-space reasoning for autonomous driving in unstructured mining environments.