A Health Monitoring System with Hybrid Bayesian Network for Autonomous Vehicle Autonomous Vehicles should transform the urban transport scenario. However, to be able to navigate completely autonomously, they also need to deal with faults that its components are subject to. Therefore, Health Monitoring System, is a component of the autonomous system which constantly monitor the integrity of those components, so that safety measures are taken as soon as an abnormal condition is detected. This paper presents a Health Monitoring System using Componentbased Hierarchical approach and Hybrid Bayesian Networks with Residue Evidence for Fault Detection and Diagnosis in lateral and longitudinal controllers, and also in the GPS sensor. Finally, the results demonstrated the reliability of the proposed methods for Fault Detection and Diagnosis, and also highlighted the importance of safety protocols for Autonomous Vehicles.

Fast Runtime Monitoring, Replanning, and Recovery for Safe Autonomous System Operations In this paper, we present a fast runtime monitoring framework for safety assurance during autonomous system operations in uncertain environments. Modern unmanned vehicles rely on periodic sensor measurements for motion planning and control. However, a vehicle may not always be able to obtain its state information due to various reasons such as sensor failures, signal occlusions, and communication problems. To guarantee the safety of a system during these circumstances under the presence of disturbance and noise, we propose a novel fast reachability analysis approach that leverages Gaussian process regression theory to predict future states of the system at runtime. We also propose a selfeventtriggered monitoring and replanning approach which leverages our fast reachability scheme to recover the system when needed and replan its trajectory to guarantee safety constraints (i.e., the system will not collide with any obstacles). Our technique is validated both with simulations and experiments on unmanned aerial vehicles case studies in cluttered environments under the effect of unknown wind disturbance at runtime.