In this paper, we present a general methodology to estimate safety related parameter values of cooperative cyber-physical system-of-systems. As a case study, we consider a vehicle platoon model equipped with a novel distributed protocol for coordinated emergency braking. The estimation methodology is based on learning-based testing; which is an approach to automated requirements testing that combines machine learning with model checking. Our methodology takes into account vehicle dynamics, control algorithm design, inter-vehicle communication protocols and environmental factors such as message packet loss rates. Empirical measurements from road testing of vehicle-to-vehicle communication in a platoon are modeled and used in our case study. We demonstrate that the minimum global time headway for our platoon model equipped with the CEBP function scales well with respect to platoon size.
Learning-based testing (LBT) is a paradigm for fully automated requirements testing that combines machine learning with model-checking techniques. LBT has been shown to be effective for unit and integration testing of safety critical components in cyber-physical systems , e.g. automotive ECU software. We consider the challenges faced, and some initial results obtained in an effort to scale up LBT to testing cooperative open cyber-physical systems-of-systems (CO-CPS). For this we focus on a case study of testing safety and performance properties of multi-vehicle platoons.