This demo showcases the features of an adaptive middleware framework for resource constrained autonomous robots like DeepNNCar (Figure 1). These robots use Learning Enabled Components (LECs), trained with deep learning models to perform control actions. However, these LECs do not provide any safety guarantees and testing them is challenging. To overcome these challenges, we have developed an adaptive middleware framework that (1) augments the LEC with safety controllers that can use different weighted simplex strategies to improve the systems safety guarantees, and (2) includes a resource manager to monitor the resource parameters (temperature, CPU Utilization), and offload tasks at runtime. Using DeepNNCar we will demonstrate the framework and its capability to adaptively switch between the controllers and strategies based on its safety and speed performance.