Predictive Uncertainty for Runtime Assurance of a Real-Time Computer Vision-Based Landing System

• URL: http://arxiv.org/abs/2508.09732v1
• Date: Wed, 13 Aug 2025 11:56:22 GMT
• Title: Predictive Uncertainty for Runtime Assurance of a Real-Time Computer Vision-Based Landing System
• Authors: Romeo Valentin, Sydney M. Katz, Artur B. Carneiro, Don Walker, Mykel J. Kochenderfer,
• Abstract summary: We present a vision-based pipeline for aircraft pose estimation from runway images.<n>Our approach features three key innovations: (i) an efficient, flexible neural architecture based on a spatial Soft Argmax operator for probabilistic keypoint regression, supporting diverse vision backbones with real-time inference; (ii) a principled loss function producing predictive uncertainties, which are evaluated via sharpness and calibration metrics; and (iii) an adaptation of Residual-based Receiver Autonomous Integrity Monitoring (RAIM) enabling runtime detection and rejection of faulty model outputs.
• Score: 32.06417142452719
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in data-driven computer vision have enabled robust autonomous navigation capabilities for civil aviation, including automated landing and runway detection. However, ensuring that these systems meet the robustness and safety requirements for aviation applications remains a major challenge. In this work, we present a practical vision-based pipeline for aircraft pose estimation from runway images that represents a step toward the ability to certify these systems for use in safety-critical aviation applications. Our approach features three key innovations: (i) an efficient, flexible neural architecture based on a spatial Soft Argmax operator for probabilistic keypoint regression, supporting diverse vision backbones with real-time inference; (ii) a principled loss function producing calibrated predictive uncertainties, which are evaluated via sharpness and calibration metrics; and (iii) an adaptation of Residual-based Receiver Autonomous Integrity Monitoring (RAIM), enabling runtime detection and rejection of faulty model outputs. We implement and evaluate our pose estimation pipeline on a dataset of runway images. We show that our model outperforms baseline architectures in terms of accuracy while also producing well-calibrated uncertainty estimates with sub-pixel precision that can be used downstream for fault detection.

Related papers

• UNet-Based Keypoint Regression for 3D Cone Localization in Autonomous Racing [0.0]
  We present a UNet-based neural network for keypoint detection on cones.<n>Our approach enables accurate cone position estimation and the potential for color prediction.
  arXiv  Detail & Related papers  (2026-02-25T13:34:56Z)
• Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization [53.82400605816587]
  Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.<n>A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios.<n>We introduce Continual AQA (CAQA), which equips with Continual Learning capabilities to handle evolving distributions.
  arXiv  Detail & Related papers  (2025-10-08T10:09:47Z)
• From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM [52.64097278841485]
  Review examines how incorporating learning and observational biases through physics-informed modeling and data strategies can guide models toward physically consistent and reliable predictions.<n>Fast adaptation methods including meta-learning and few-shot learning are reviewed alongside domain generalization techniques.
  arXiv  Detail & Related papers  (2025-09-25T14:15:43Z)
• Bayesian-Driven Graph Reasoning for Active Radio Map Construction [96.08082552413117]
  We propose an uncertainty-aware radio map reconstruction framework that explicitly leverages graph-based reasoning tailored for waypoint navigation.<n>Our approach integrates two key deep learning components: (1) a Bayesian neural network that estimates spatial uncertainty in real time, and (2) an attention-based reinforcement learning policy that performs global reasoning.<n> Experimental results show that URAM improves reconstruction accuracy by up to 34% over existing baselines.
  arXiv  Detail & Related papers  (2025-07-29T03:32:01Z)
• OCCUQ: Exploring Efficient Uncertainty Quantification for 3D Occupancy Prediction [9.742801351723482]
  Current methods often overlook uncertainties arising from adversarial conditions or distributional shifts.<n>We propose an efficient adaptation of an uncertainty estimation technique for 3D occupancy prediction.<n>Our approach consistently demonstrates reliable uncertainty measures, indicating its potential for enhancing the robustness of autonomous driving systems.
  arXiv  Detail & Related papers  (2025-03-13T17:50:07Z)
• AdaOcc: Adaptive Forward View Transformation and Flow Modeling for 3D Occupancy and Flow Prediction [56.72301849123049]
  We present our solution for the Vision-Centric 3D Occupancy and Flow Prediction track in the nuScenes Open-Occ dataset challenge at CVPR 2024. Our innovative approach involves a dual-stage framework that enhances 3D occupancy and flow predictions by incorporating adaptive forward view transformation and flow modeling. Our method combines regression with classification to address scale variations in different scenes, and leverages predicted flow to warp current voxel features to future frames, guided by future frame ground truth.
  arXiv  Detail & Related papers  (2024-07-01T16:32:15Z)
• OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
  Trajectory prediction is fundamental in computer vision and autonomous driving. Existing approaches in this field often assume precise and complete observational data. We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
  arXiv  Detail & Related papers  (2024-04-02T18:30:29Z)
• Automatic UAV-based Airport Pavement Inspection Using Mixed Real and Virtual Scenarios [3.0874677990361246]
  We propose a vision-based approach to automatically identify pavement distress using images captured by UAVs. The proposed method is based on Deep Learning (DL) to segment defects in the image. We demonstrate that the use of a mixed dataset composed of synthetic and real training images yields better results when testing the training models in real application scenarios.
  arXiv  Detail & Related papers  (2024-01-11T16:30:07Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• USC: Uncompromising Spatial Constraints for Safety-Oriented 3D Object Detectors in Autonomous Driving [7.355977594790584]
  We consider the safety-oriented performance of 3D object detectors in autonomous driving contexts.<n>We present uncompromising spatial constraints (USC), which characterize a simple yet important localization requirement.<n>We incorporate the quantitative measures into common loss functions to enable safety-oriented fine-tuning for existing models.
  arXiv  Detail & Related papers  (2022-09-21T14:03:08Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye View [110.83289076967895]
  We present a BEV domain adaptation method based on CycleGAN that uses prior semantic classification in order to preserve the information of small objects of interest during the domain adaptation process. The quality of the generated BEVs has been evaluated using a state-of-the-art 3D object detection framework at KITTI 3D Object Detection Benchmark.
  arXiv  Detail & Related papers  (2021-04-22T12:47:37Z)
• An Uncertainty-based Human-in-the-loop System for Industrial Tool Wear Analysis [68.8204255655161]
  We show that uncertainty measures based on Monte-Carlo dropout in the context of a human-in-the-loop system increase the system's transparency and performance. A simulation study demonstrates that the uncertainty-based human-in-the-loop system increases performance for different levels of human involvement.
  arXiv  Detail & Related papers  (2020-07-14T15:47:37Z)
• Confidence-rich grid mapping [19.530047371535147]
  Representing the environment is a fundamental task in enabling robots to act autonomously in unknown environments. We present confidence-rich mapping, a new algorithm for spatial grid-based mapping of the 3D environment. We show in real-world experiments that, in addition to achieving maps that are more accurate than traditional methods, the proposed filtering scheme demonstrates a much higher level of consistency between its error and the reported confidence.
  arXiv  Detail & Related papers  (2020-06-29T00:21:30Z)

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.