Resiliency Characterization of Navigation Systems for Intelligent Transportation Applications

Automated Driving Systems (ADS) are expected to be an integral component of a future safe and efficient intelligent transportation system. ADSs assume strategic and tactical maneuvering decisions, and associated vehicle control functions traditionally performed by human drivers. Navigation systems supporting this high level of automation are safety critical and must meet requirements imposed by the use-case nominal operation conditions. These systems must also be resilient to certain intentional and unintentional threats encountered during operation. While there have been past and ongoing efforts to determine PNT safety performance needs, an approach to quantify navigation system resiliency to intentional threats is still lacking. In this paper we develop such approach and introduce two resiliency metrics to quantitatively assess automated vehicle performance, with a primary focus on ADS with SAE Automation Level 4 ( L4). Our resiliency metrics build on formal definitions of integrity, accuracy, availability, and continuity, adapting concepts used in commercial aviation to also apply to road applications. In our analysis, the key is to distinguish faults (for which a prior probability can be defined) from threats (for which a prior cannot be defined). A simulation of an ADS L4 multilane highway application with vehicle-to-vehicle and vehicle-to-infrastructure communication quantitatively demonstrates how our proposed approach allows for safe operation during a time-limited transition immediately after the introduction of a threat and also for persistent threats (via reduced capacity mitigation). This simulation will also illustrate how, for a particular navigation system, the two complementary resiliency metrics can be used to quantify the increased risk during the time-limited transition as well as the capacity degradation level for safe steady state safe operations.