用视觉模型集成提升安全控制滤波器的准确性和泛化能力。
Learning Ensembles of Vision-based Safety Control Filters
- 采用多种预训练视觉模型构建集成滤波器，提升决策可靠性。
- 在DeepAccident数据集上，集成模型分类准确率优于单个模型。
- 适合关注自动驾驶安全验证的研究者和工程师。
控制系统的安全滤波器可修正违反安全约束的常规控制指令。在不确定且复杂的环境中，基于视觉观测设计此类滤波器极具挑战性。尽管已有若干基于深度学习的方法提出，但目前尚无法形式化验证所学滤波器是否满足保障系统安全的关键性质。为此，本文受强化学习中集成方法成功的启发，通过实验探究集成策略在提升滤波器准确性及分布外泛化能力方面的有效性，作为迈向更可靠安全机制的一步。我们测试了多种预训练视觉表征模型作为滤波器主干、不同的训练方法和输出聚合技术，在DeepAccident数据集上比较不同配置的集成模型与单一模型及其大型单模型基线的表现，评估其在区分安全与不安全状态和控制方面的性能。结果表明，多样化的集成模型相比单个模型具有更高的状态与控制分类准确率。
原文摘要 · Abstract (English)
Safety filters in control systems correct nominal controls that violate safety constraints. Designing such filters as functions of visual observations in uncertain and complex environments is challenging. Several deep learning-based approaches to tackle this challenge have been proposed recently. However, formally verifying that the learned filters satisfy critical properties that enable them to guarantee the safety of the system is currently beyond reach. Instead, in this work, motivated by the success of ensemble methods in reinforcement learning, we empirically investigate the efficacy of ensembles in enhancing the accuracy and the out-of-distribution generalization of such filters, as a step towards more reliable ones. We experiment with diverse pre-trained vision representation models as filter backbones, training approaches, and output aggregation techniques. We compare the performance of ensembles with different configurations against each other, their individual member models, and large single-model baselines in distinguishing between safe and unsafe states and controls in the DeepAccident dataset. Our results show that diverse ensembles have better state and control classification accuracies compared to individual models.
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