Uncertainty Estimation for Deep Reconstruction in Aquatic Disaster Scenarios with Autonomous Vehicles
Summary: arXiv:2604.06387v1 Announce Type: cross
Abstract
Accurate reconstruction of environmental scalar fields from sparse onboard observations is essential for autonomous vehicles engaged in aquatic monitoring. Beyond point estimates, principled uncertainty quantification is critical for active sensing strategies such as Informative Path Planning, where epistemic uncertainty drives data collection decisions. This paper compares Gaussian Processes, Monte Carlo Dropout, Deep Ensembles, and Evidential Deep Learning for simultaneous scalar field reconstruction and uncertainty decomposition under three perceptual models representative of real sensor modalities.
Key Findings
The research findings reveal significant insights into the performance of various methods for uncertainty-aware field reconstruction:
- Evidential Deep Learning: This method achieved the best reconstruction accuracy and uncertainty calibration across all sensor configurations at the lowest inference cost.
- Gaussian Processes: These models are fundamentally limited by their stationary kernel assumption and become intractable as observation density grows.
- Monte Carlo Dropout: This method provides a stochastic approach to uncertainty estimation but falls short in comparison to Evidential Deep Learning.
- Deep Ensembles: While this technique offers improved accuracy over simpler methods, it still does not match the efficiency of Evidential Deep Learning.
Implications for Autonomous Vehicles
The ability to accurately reconstruct environmental conditions is crucial for the deployment of autonomous vehicles, especially in disaster scenarios where real-time decision-making is essential. The findings of this study suggest that incorporating Evidential Deep Learning into the sensing architecture of autonomous systems can significantly enhance their capability to operate effectively in uncertain environments.
Conclusion
In conclusion, the paper supports Evidential Deep Learning as the preferred method for uncertainty-aware field reconstruction in real-time autonomous vehicle deployments. By addressing the limitations associated with other methods and demonstrating superior performance in various configurations, this approach provides a robust framework for enhancing the operational efficiency of autonomous vehicles in aquatic monitoring and disaster scenarios.
Future Work
Further research is warranted to explore the scalability of Evidential Deep Learning in larger, more complex environments and to integrate these findings into real-world autonomous vehicle systems. Additionally, investigating hybrid approaches that combine the strengths of different methods may yield even more effective solutions for uncertainty estimation and field reconstruction.