Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems

TL;DR

This paper introduces a dynamic simplex approach with online controller switching for autonomous cyber-physical systems, enhancing safety and performance by enabling two-way switching between controllers based on real-time conditions.

Contribution

It proposes a novel online controller switching strategy modeled as a semi-Markov decision process, allowing adaptive and reversible switching for improved safety and performance.

Findings

Fewer collisions in autonomous vehicle simulations.

Higher performance compared to existing methods.

Effective handling of dynamic and uncertain environments.

Abstract

Learning Enabled Components (LEC) have greatly assisted cyber-physical systems in achieving higher levels of autonomy. However, LEC's susceptibility to dynamic and uncertain operating conditions is a critical challenge for the safety of these systems. Redundant controller architectures have been widely adopted for safety assurance in such contexts. These architectures augment LEC "performant" controllers that are difficult to verify with "safety" controllers and the decision logic to switch between them. While these architectures ensure safety, we point out two limitations. First, they are trained offline to learn a conservative policy of always selecting a controller that maintains the system's safety, which limits the system's adaptability to dynamic and non-stationary environments. Second, they do not support reverse switching from the safety controller to the performant controller, even when the threat to safety is no longer present. To address these limitations, we propose a dynamic simplex strategy with an online controller switching logic that allows two-way switching. We consider switching as a sequential decision-making problem and model it as a semi-Markov decision process. We leverage a combination of a myopic selector using surrogate models (for the forward switch) and a non-myopic planner (for the reverse switch) to balance safety and performance. We evaluate this approach using an autonomous vehicle case study in the CARLA simulator using different driving conditions, locations, and component failures. We show that the proposed approach results in fewer collisions and higher performance than state-of-the-art alternatives.