Current Self-Adaptive Systems (SASs) such as Autonomous Vehicles (AVs) are systems able to deal with highly complex contexts. However, due to the use of static feedback loops they are not able to respond to unanticipated situations such as sensor faults. Previously, we have proposed HAFLoop (Highly Adaptive Feedback control Loop), an architecture for adaptive loops in SASs. In this paper, we incorporate HAFLoop into an AV solution that leverages machine learning techniques to determine the best monitoring strategy at runtime. We have evaluated our solution using real vehicles. Evaluation results are promising and demonstrate the great potential of our proposal.