Learning Ensembles of Vision-based Safety Control Filters

TL;DR

This paper explores the use of ensemble methods with vision-based safety control filters to improve accuracy and generalization in safety-critical control systems, addressing verification challenges.

Contribution

It empirically demonstrates that diverse ensembles enhance safety state classification accuracy over individual models in vision-based control filters.

Findings

Ensembles outperform individual models in safety classification accuracy.

Diverse ensemble configurations improve out-of-distribution generalization.

Ensemble methods show promise for more reliable vision-based safety filters.

Abstract

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.