UnsDrive: Towards Robust End-to-End Autonomous Driving in Unstructured Scenes

• URL: http://arxiv.org/abs/2608.09098v1
• Date: Mon, 10 Aug 2026 03:56:30 GMT
• Title: UnsDrive: Towards Robust End-to-End Autonomous Driving in Unstructured Scenes
• Abstract summary: We propose UnsDrive, an end-to-end planner designed for unstructured mining scenes.<n>UnsDrive explicitly models occupied, free, and unknown space using multi-frame visibility cues.<n>We also present MineLoop, a mining-oriented closed-loop simulator for evaluating autonomous driving under irregular road geometry.
• Score: 4.811153938367647
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model [2.4196046716427393]
  OWMDrive is a generative end-to-end driving framework built upon an Occupancy World Model for multi-step 3D occupancy forecasting.<n>Conditioned on both current observations and predicted future states, OWMDrive iteratively refines trajectory candidates to generate a reinforced driving trajectory.
  arXiv  Detail & Related papers  (2026-06-29T15:05:41Z)
• GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving [56.50032398571235]
  GraphWorld is an E2E-AD framework that explicitly enhances long-horizon planning through latent world modeling.<n>We present a World-State-Conditioned Planning that learns ego-centric latent world representations by modeling interactions between an ego vehicle and surrounding agents.<n>This latent world state captures key interaction dynamics and safety-relevant semantics, and serves as a conditioning signal to guide long-horizon, safety-aware trajectory planning.
  arXiv  Detail & Related papers  (2026-06-15T06:26:46Z)
• Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives [18.838887346129916]
  We propose BeyondDrive, a failure-aware imitation learning framework that jointly learns from successful and failed driving behaviors.<n>First, we introduce a flow matching-based negative trajectory generator that synthesizes safety-critical yet expert-proximate trajectories.<n>Second, we develop a diversity-aware sampling strategy that mitigates mode collapse and improves coverage of diverse failure modes.<n>Third, we propose a Repulsive Distance Loss that simultaneously attracts predictions toward expert demonstrations while repelling them from hard negative trajectories.
  arXiv  Detail & Related papers  (2026-05-19T12:41:47Z)
• Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations [122.22391796628408]
  We present Bench2Drive-Robust, to our knowledge the first device-centric robustness benchmark for closed-loop end-to-end autonomous driving.<n>We systematically evaluate deployment-oriented perturbations arising from three major sources: camera-stream failures, ego-state estimation errors, and compute-induced control delay.<n>Our results show that these deployment-related perturbations can substantially degrade closed-loop driving performance.
  arXiv  Detail & Related papers  (2026-05-18T08:45:24Z)
• MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving [62.43744546817599]
  Vision-language--action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings.<n>We propose MAPLE, a novel framework for reactive, multi-agent rollout of a dynamic driving scenario in the latent space of the VLA model.<n> MAPLE achieves state-of-the-art driving performance on Bench2Drive and demonstrates scalable, closed-loop multi-agent play for robust E2E autonomous driving systems.
  arXiv  Detail & Related papers  (2026-05-13T23:35:14Z)
• FeaXDrive: Feasibility-aware Trajectory-Centric Diffusion Planning for End-to-End Autonomous Driving [9.654458127972736]
  FeaXDrive is a feasibility-aware trajectory-centric diffusion planning method for end-to-end autonomous driving.<n>Built on this trajectory-centric formulation, FeaXDrive integrates adaptive curvature-constrained training to improve intrinsic geometric and kinematic feasibility.<n>Experiments on the NAVSIM benchmark show that FeaXDrive achieves strong closed-loop planning performance.
  arXiv  Detail & Related papers  (2026-04-14T12:24:54Z)
• SUPER-AD: Semantic Uncertainty-aware Planning for End-to-End Robust Autonomous Driving [36.91878828972102]
  We propose a camera-only E2E framework that estimates aleatoric uncertainty directly in BEV space and incorporates it into planning.<n>Our method produces a dense, uncertainty-aware drivability map that captures both semantic structure and geometric layout at pixel-level resolution.
  arXiv  Detail & Related papers  (2025-11-28T03:50:44Z)
• FlowDrive: Energy Flow Field for End-to-End Autonomous Driving [50.89871153094958]
  FlowDrive is a novel framework that introduces physically interpretable energy-based flow fields to encode semantic priors and safety cues into the BEV space.<n> Experiments on the NAVSIM v2 benchmark demonstrate that FlowDrive achieves state-of-the-art performance with anS of 86.3, surpassing prior baselines in both safety and planning quality.
  arXiv  Detail & Related papers  (2025-09-17T13:51:33Z)
• Enhancing End-to-End Autonomous Driving with Latent World Model [78.22157677787239]
  We propose a novel self-supervised learning approach using the LAtent World model (LAW) for end-to-end driving.<n> LAW predicts future scene features based on current features and ego trajectories.<n>This self-supervised task can be seamlessly integrated into perception-free and perception-based frameworks.
  arXiv  Detail & Related papers  (2024-06-12T17:59:21Z)
• QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving [33.609780917199394]
  Self-driving vehicles must understand its environment to determine appropriate action. Traditional systems rely on object detection to find agents in the scene. We present a unified, interpretable, and efficient autonomy framework that moves away from cascading modules that first perceive occupancy relevant-temporal autonomy.
  arXiv  Detail & Related papers  (2024-04-01T21:11:43Z)
• Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving [100.57791628642624]
  We introduce a safety guaranteed learning framework for vision-based end-to-end autonomous driving. We design a learning system equipped with differentiable control barrier functions (dCBFs) that is trained end-to-end by gradient descent.
  arXiv  Detail & Related papers  (2022-03-04T16:14:33Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.