Leveraging Collision Avoidance Robustness to Establish Situational Awareness Requirements: A Closed Loop Simulator Approach

Using a maritime traffic simulator to analyze the performance of autonomous navigation functions, specifically Collision Avoidance (CA), and how its performance depends on the quality of input from the Situational Awareness (SA) system is the target of this paper. To that end, a closed-loop simulator is developed that can take the SA "ground-truth" of a given maritime traffic situation, add modeled noise and other perturbations to ship positioning and world-states, feeding this as input to the CA system. For several scenarios, the quality (noise and error rate) of the input to the CA system is systematically varied, and performance is evaluated with respect to collision risk and compliance with a selected set of the International Regulations for Preventing Collisions at Sea (COLREG). This type of robustness assessment is crucial as it directly governs performance requirements for the SA system and its physical sensors used for sensing the environment and monitoring the condition of the ship. This paper provides examples of how quantifying the robustness of CA systems can determine performance criteria for SA systems.

Keywords: Autonomy, Assurance, Safety, Navigation, Robustness, Simulation testing, Collision avoidance.