  Autonomous vehicles (AV) depend on the sensors like RADAR and camera for the
perception of the environment, path planning, and control. With the increasing
autonomy and interactions with the complex environment, there have been growing
concerns regarding the safety and reliability of AVs. This paper presents a
Systems-Theoretic Process Analysis (STPA) based fault injection framework to
assess the resilience of an open-source driving agent, called openpilot, under
different environmental conditions and faults affecting sensor data. To
increase the coverage of unsafe scenarios during testing, we use a strategic
software fault-injection approach where the triggers for injecting the faults
are derived from the unsafe scenarios identified during the high-level hazard
analysis of the system. The experimental results show that the proposed
strategic fault injection approach increases the hazard coverage compared to
random fault injection and, thus, can help with more effective simulation of
safety-critical faults and testing of AVs. In addition, the paper provides
insights on the performance of openpilot safety mechanisms and its ability in
timely detection and recovery from faulty inputs.
