Publication
Adaptive monitoring for autonomous vehicles using the HAFLoop architecture
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.
Acknowledgments
Thanks to the Chalmers Revere vehicle laboratory for the technical support provided during the implementation and experimentation phases described in this work. This work was supported by the Swedish organisations AstaZero and SAFER under Grant Project A-0034; Campus Energia (UPC) under program Ajuts de mobilitat internacional; the Spanish project GENESIS under Grant TIN2016-79269-R; the Mexican council CONACYT.