A Systematic Approach to Analyzing Perception Architectures in Autonomous Vehicles

Simulations are commonly used to validate the design of autonomous systems. However, as these systems are increasingly deployed into safety-critical environments with aleatoric uncertainties, and with the increase in components that employ machine learning algorithms with epistemic uncertainties, validation methods which consider uncertainties are lacking. We present an approach that evaluates signal propagation in logical system architectures, in particular environment perception-chains, focusing on effects of uncertainty to determine functional limitations. The perception based autonomous driving systems are represented by connected elements to constitute a certain functionality. The elements are based on (meta-)models to describe technical components and their behavior. The surrounding environment, in which the system is deployed, is modeled by parameters that are derived from a quasi-static scene. All parameter variations completely define input-states for the designed perception architecture. The input-states are treated as random variables inside the model of components to simulate aleatoric/epistemic uncertainty. The dissimilarity between the model-input and -output serves as measure for total uncertainty present in the system. The uncertainties are propagated through consecutive components and calculated by the same manner. The final result consists of input-states which model uncertainty effects for the specified functionality and therefore highlight shortcomings of the designed architecture.