Operational Context Change Propagation Prediction on Autonomous Vehicles Architectures

Abstract

Autonomous Vehicles (AV) are designed to operate in a specific Operational Context (OC), and the adaptability of the vehicle's architecture to its OC is considered a significant success criterion of the design. AV design projects are rarely started from scratch and are often based on reference architectures. As such, the reference architecture must be modified and adapted to the OC. The current literature on engineering change propagation does not provide a method to identify and anticipate the impact of OC changes on the AV reference architecture. This paper proposes a two-step method for OC change propagation: (1) Analyzing the direct impact of OC change and (2) evaluate the probabilities of indirect change propagation. The direct impact is assessed following a propagation path based upon a model mapping between an OC Ontology, operational situations, and Functional Chains. The effects of Functional Chain changes on the AV components are analyzed and evaluated by domain experts with Types of Changes and associated probabilities. A Bayesian Network is proposed to calculate the probabilities of indirect change propagation between component Types of Changes. The method's applicability and efficiency are validated on a real case design of AV architecture where the probabilities of the system components undergoing Types of Changes are evaluated.