Artificial intelligence and deep learning have demonstrated highly promising results in challenging problems, such as autonomous or assisted driving. One challenge in the integration of these solutions in real-life applications, is that they often operate in a resource-constrained edge environment. Another important challenge is the ability of the AI system to adapt and expand its abilities in a constantly changing environment. Constant changes could potentially cause significant deterioration of a model's effectiveness, a phenomenon called Catastrophic Forgetting. In this paper, we propose a Continual Learning framework for efficient and continuous update of a road sign classification system for assisted or autonomous driving. Our proposition considers the limitations of edge computing and utilizes a cloud infrastructure. Test results show that the our proposition is capable of expanding an edge models knowledge in a stable manner.