Towards Cognitive Vehicles: GNSS-free Localization using Visual Anchors

Abstract

Cognitive vehicles (CV) differ from smart vehicles (SV) in a way that they don't just rely on the sensors' readings and follow rigorously the patterns and functions already preprogrammed externally. CVs utilize the different sensors as a source of information, which needs to be processed and turned into intelligence and perception. CVs learn at a scale, make assumptions, predict outcomes, and learn from experience rather than being explicitly programmed. In this work, we attempt to present a model that duplicates the cognitive process through which humans can self-localize. We present an innovative GNSS-free solution for vehicle self-localization based on detection pattern recognition of visual anchors. The proposed cognitive approach is successfully tested in different routes taken from a real urban environment. The system location estimates are compared with the GPS reported locations and show promising performances.