Belief Space Metareasoning for Exception Recovery Due to the complexity of the real world, autonomous systems use decisionmaking models that rely on simplifying assumptions to make them computationally tractable and feasible to design. However, since these limited representations cannot fully capture the domain of operation, an autonomous system may encounter unanticipated scenarios that cannot be resolved effectively. We first formally introduce an introspective autonomous system that uses belief space metareasoning to recover from exceptions by interleaving a main decision process with a set of exception handlers. We then apply introspective autonomy to autonomous driving. Finally, we demonstrate that an introspective autonomous vehicle is effective in simulation and on a fully operational prototype.

From Specifications to Behavior: Maneuver Verification in a Semantic State Space To realize a market entry of autonomous vehicles in the foreseeable future, the behavior planning system will need to abide by the same rules that humans follow. Product liability cannot be enforced without a proper solution to the approval trap. In this paper, we define a semantic abstraction of the continuous space and formalize traffic rules in linear temporal logic (LTL). Sequences in the semantic state space represent maneuvers a highlevel planner could choose to execute. We check these maneuvers against the formalized traffic rules using runtime verification. By using the standard model checker NuSMV, we demonstrate the effectiveness of our approach and provide runtime properties for the maneuver verification. We show that highlevel behavior can be verified in a semantic state space to fulfill a set of formalized rules, which could serve as a step towards safety of the intended functionality.