用证据深度学习提升无人艇在灾后水域的环境场重建与不确定性估计
Uncertainty Estimation for Deep Reconstruction in Actuatic Disaster Scenarios with Autonomous Vehicles
- 采用证据深度学习实现环境场重建与不确定性的联合建模
- 在多种传感器条件下，该方法精度最高且推理成本最低
- 适合实时自主车辆部署，尤其适用于灾后监测场景
从车载稀疏观测中准确重构环境标量场，对执行水下监测任务的自动驾驶车辆至关重要。除点估计外，合理的不确定性量化对于主动感知策略（如信息性路径规划）尤为关键，其中认知不确定性驱动数据采集决策。本文对比了高斯过程、蒙特卡洛丢弃、深度集成和证据深度学习，在三种代表真实传感器模态的感知模型下，同时进行标量场重建与不确定性分解的表现。结果表明，证据深度学习在所有传感器配置中均取得最佳重建精度与不确定性校准效果，且推理开销最低；而高斯过程因平稳核假设存在根本局限，观测密度增加时计算不可行。这些发现支持证据深度学习作为实时自动驾驶车辆部署中不确定性感知场重建的首选方法。
原文摘要 · Abstract (English)
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. Results show that Evidential Deep Learning achieves the best reconstruction accuracy and uncertainty calibration across all sensor configurations at the lowest inference cost, while Gaussian Processes are fundamentally limited by their stationary kernel assumption and become intractable as observation density grows. These findings support Evidential Deep Learning as the preferred method for uncertainty-aware field reconstruction in real-time autonomous vehicle deployments.
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