# Confidence-Gated Vision-Only Heading Alignment for UAV-UGV Cooperative Systems

arXiv: https://arxiv.org/abs/2607.18713 · HTML: https://mlsanity.com/paper/2607.18713/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 21 Jul 2026.

## Builds on (most cited first, 16 of 16)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017)
- [SelectiveNet: A Deep Neural Network with an Integrated Reject Option](https://mlsanity.com/paper/1901.09192/) (2019)
- [Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control](https://mlsanity.com/paper/1811.06817/) (2018)
- [Leveraging Uncertainty in Deep Learning for Selective Classification](https://mlsanity.com/paper/1905.09509/) (2019)
- [Visual Servoing Approach for Autonomous UAV Landing on a Moving Vehicle](https://mlsanity.com/paper/2104.01272/) (2021)
- [An Experimental Study of Trojan Vulnerabilities in UAV Autonomous Landing](https://mlsanity.com/paper/2510.20932/) (2025)
- [Visual Heading Prediction for Autonomous Aerial Vehicles](https://mlsanity.com/paper/2512.09898/) (2025)
- [Evolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons](https://mlsanity.com/paper/2603.21544/) (2026)
- [Autonomous UAV Landing System Based on Visual Navigation](https://mlsanity.com/paper/1910.13174/) (2019)
- [A Validation Strategy for Deep Learning Models: Evaluating and Enhancing Robustness](https://mlsanity.com/paper/2509.19197/) (2025)

## Abstract (arXiv)

Vision-based heading prediction is useful for UAV--UGV cooperation, but accurate prediction alone does not guarantee that every predicted heading should be issued directly as a control command. This paper investigates the decision problem of when and how a fixed vision-based heading predictor should be trusted for command issuance. A lightweight confidence-gated framework is proposed in which execution decisions are made using two interpretable reliability proxies derived from the perception stream: bounding-box area as a visibility-related proxy and short-window variation in predicted heading as a stability-related proxy. During low-confidence intervals, the framework compares the baseline freeze-HOLD policy with a bounded-blend fallback that updates the issued command conservatively. The method is evaluated on a real UAV--UGV dataset under clean and perturbed conditions. The results show that confidence gating creates a clear trade-off among execution rate, executed-frame accuracy, issued-command accuracy, and smoothness. The results further show that sparse execution can cause severe stale-command error under the baseline freeze-HOLD policy, whereas the bounded-blend fallback substantially improves command-level behavior under the same gate decisions. These findings highlight that reliable perception-driven autonomy depends not only on prediction accuracy, but also on decision-aware command issuance during low-confidence