  Fully autonomous vehicles (AVs), i.e., AVs with autonomy level 5, are
expected to dominate road transportation in the near-future and contribute
trillions of dollars to the global economy. The general public, government
organizations, and manufacturers all have significant concern regarding
resiliency and safety standards of the autonomous driving system (ADS) of AVs .
In this work, we proposed and developed (a) `Kayotee' - a fault injection-based
tool to systematically inject faults into software and hardware components of
the ADS to assess the safety and reliability of AVs to faults and errors, and
(b) an ontology model to characterize errors and safety violations impacting
reliability and safety of AVs. Kayotee is capable of characterizing fault
propagation and resiliency at different levels - (a) hardware, (b) software,
(c) vehicle dynamics, and (d) traffic resilience. We used Kayotee to study a
proprietary ADS technology built by Nvidia corporation and are currently
applying Kayotee to other open-source ADS systems.
