Learning Ensembles of Vision-based Safety Control Filters
This work addresses the problem of improving reliability in safety-critical control systems for robotics or autonomous vehicles, but it is incremental as it builds on existing ensemble methods without formal verification.
The paper tackled the challenge of designing vision-based safety control filters in uncertain environments by empirically investigating the efficacy of ensemble methods to enhance accuracy and out-of-distribution generalization. The results showed that diverse ensembles achieved better state and control classification accuracies compared to individual models on the DeepAccident dataset.
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.