Vision-Based Risk Aware Emergency Landing for UAVs in Complex Urban Environments

• URL: http://arxiv.org/abs/2505.20423v1
• Date: Mon, 26 May 2025 18:16:21 GMT
• Title: Vision-Based Risk Aware Emergency Landing for UAVs in Complex Urban Environments
• Authors: Julio de la Torre-Vanegas, Miguel Soriano-Garcia, Israel Becerra, Diego Mercado-Ravell,
• Abstract summary: We propose a risk-aware approach that harnesses semantic segmentation to continuously evaluate potential hazards in the drone's field of view.<n>Our method adaptively identifies a stable Safe Landing Zone (SLZ) despite moving critical obstacles.<n>A control system then guides the UAV toward this low-risk region, employing altitude-dependent safety thresholds and temporal landing point stabilization.
• Score: 0.46873264197900916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Landing safely in crowded urban environments remains an essential yet challenging endeavor for Unmanned Aerial Vehicles (UAVs), especially in emergency situations. In this work, we propose a risk-aware approach that harnesses semantic segmentation to continuously evaluate potential hazards in the drone's field of view. By using a specialized deep neural network to assign pixel-level risk values and applying an algorithm based on risk maps, our method adaptively identifies a stable Safe Landing Zone (SLZ) despite moving critical obstacles such as vehicles, people, etc., and other visual challenges like shifting illumination. A control system then guides the UAV toward this low-risk region, employing altitude-dependent safety thresholds and temporal landing point stabilization to ensure robust descent trajectories. Experimental validation in diverse urban environments demonstrates the effectiveness of our approach, achieving over 90% landing success rates in very challenging real scenarios, showing significant improvements in various risk metrics. Our findings suggest that risk-oriented vision methods can effectively help reduce the risk of accidents in emergency landing situations, particularly in complex, unstructured, urban scenarios, densely populated with moving risky obstacles, while potentiating the true capabilities of UAVs in complex urban operations.

Related papers

• An Approach to Technical AGI Safety and Security [72.83728459135101]
  We develop an approach to address the risk of harms consequential enough to significantly harm humanity.<n>We focus on technical approaches to misuse and misalignment.<n>We briefly outline how these ingredients could be combined to produce safety cases for AGI systems.
  arXiv  Detail & Related papers  (2025-04-02T15:59:31Z)
• From Shadows to Safety: Occlusion Tracking and Risk Mitigation for Urban Autonomous Driving [1.8434042562191815]
  This research builds upon and extends existing approaches in risk-aware motion planning and occlusion tracking.<n>We enhance a phantom agent-centric model by incorporating sequential reasoning to track occluded areas and predict potential hazards.<n> Simulations demonstrate that the proposed approach improves situational awareness and balances proactive safety with efficient traffic flow.
  arXiv  Detail & Related papers  (2025-04-02T06:48:50Z)
• Risk Assessment for Autonomous Landing in Urban Environments using Semantic Segmentation [0.0]
  We propose employing the SegFormer, a state-of-the-art visual transformer network, for semantic segmentation of urban environments. The proposed strategy is validated through several case studies. We believe will help unleash the full potential of UAVs on civil applications within urban areas.
  arXiv  Detail & Related papers  (2024-10-16T19:34:03Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes [57.319845580050924]
  We propose a reinforcement learning framework that combines risk-sensitive control with an adaptive action space curriculum. We show that our algorithm is capable of learning high-speed policies for a real-world off-road driving task.
  arXiv  Detail & Related papers  (2024-05-07T23:32:36Z)
• A Counterfactual Safety Margin Perspective on the Scoring of Autonomous Vehicles' Riskiness [52.27309191283943]
  This paper presents a data-driven framework for assessing the risk of different AVs' behaviors. We propose the notion of counterfactual safety margin, which represents the minimum deviation from nominal behavior that could cause a collision.
  arXiv  Detail & Related papers  (2023-08-02T09:48:08Z)
• HALO: Hazard-Aware Landing Optimization for Autonomous Systems [1.5414037351414311]
  This paper presents a coupled perception-planning solution which addresses the hazard detection, optimal landing trajectory generation, and contingency planning challenges. We develop and combine two novel algorithms, Hazard-Aware Landing Site Selection (HALSS) and Adaptive Deferred-Decision Trajectory Optimization (-DDTO), to address the perception and planning challenges. We demonstrate the efficacy of our approach using a simulated Martian environment and show that our coupled perception-planning method achieves greater landing success.
  arXiv  Detail & Related papers  (2023-04-04T07:20:06Z)
• Visual-based Safe Landing for UAVs in Populated Areas: Real-time Validation in Virtual Environments [0.0]
  We propose a framework for real-time safe and thorough evaluation of vision-based autonomous landing in populated scenarios. We propose to use the Unreal graphics engine coupled with the AirSim plugin for drone's simulation. We study two different criteria for selecting the "best" SLZ, and evaluate them during autonomous landing of a virtual drone in different scenarios.
  arXiv  Detail & Related papers  (2022-03-25T17:22:24Z)
• Certifying Emergency Landing for Safe Urban UAV [0.0]
  Unmanned Aerial Vehicles (UAVs) have the potential to be used for many applications in urban environments. One of the main safety issues is the possibility for a failure to cause the loss of navigation capabilities. Current standards, such as the SORA published in 2019, do not consider applicable mitigation techniques to handle this kind of hazardous situations.
  arXiv  Detail & Related papers  (2021-04-30T11:47:46Z)
• Conservative Safety Critics for Exploration [120.73241848565449]
  We study the problem of safe exploration in reinforcement learning (RL) We learn a conservative safety estimate of environment states through a critic. We show that the proposed approach can achieve competitive task performance while incurring significantly lower catastrophic failure rates.
  arXiv  Detail & Related papers  (2020-10-27T17:54:25Z)
• Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings [129.80279257258098]
  Reinforcement learning (RL) in real-world safety-critical target settings like urban driving is hazardous. We propose a "safety-critical adaptation" task setting: an agent first trains in non-safety-critical "source" environments. We propose a solution approach, CARL, that builds on the intuition that prior experience in diverse environments equips an agent to estimate risk.
  arXiv  Detail & Related papers  (2020-08-15T01:40:59Z)

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.