Multi-modal Speed Limit Assistants: Combining Camera and GPS Maps

We propose a method for fusing two modalities of information for speed limit assistants: (i) camera based speed sign recognition and (ii) digitized speed limit maps combined with a GPS sensor. The fusion is based on a Bayesian framework. Here, we rely on two modeling assumptions: (i) the speed sign recognizer's score being probabilistic and (ii) a model describing speed limit sign probabilities conditioned on the map information. Speed limit assistants incorporating the proposed fusion can particularly benefit over uni-modal solutions in situations, where a solution based on a single modality is ill-posed, that is, adverse lighting or weather conditions in case of camera based speed sign recognition, and dynamic traffic guidance systems, construction zones, or incomplete maps in case of GPS maps. We give exemplary evidence of the proposed solution's effectiveness.