Towards ResponsibilitySensitive Safety of Automated Vehicles with Reachable Set Analysis One of the most critical, unsolved challenges for the introduction of automated vehicles is safety verification of planned trajectories. The most promising concepts approaching this topic are worstcase occupancy predictions based on reachable set analysis and the definition of ResponsibilitySensitive Safety (RSS) that formalizes dangerous situations and proper responses. Worstcase predictions result in overconservative behavior while the RSS approach makes strong assumptions w.r.t. lateral and longitudinal behavior. Our contribution is a first step in bringing both worlds together, to benefit from respective advantages. First, we define RSSmotivated safestates for merge and crossing scenarios, that ensure absolute safety towards leading vehicles, appropriate time gaps towards following vehicles in merging lanes and minimum clearance time of conflict zones with crossing lanes. We then show how to integrate these safety constraints in a trajectory and behavior planner using reachable sets and finally illustrate its usefulness in various simulative evaluations.

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.