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The next several years will see a large increase in automated vehicle capabilities. High levels of automation will require bi-directional transfers of control between the driver and vehicle. These control transfer situations pose one of the greatest potential safety shortfalls. One specific issue that arises is that drivers may be unfit or ill-prepared to retake control from the vehicle because of distraction, drowsiness, or intoxication. Driver state monitoring systems based on eye tracking, head tracking, and other measures may be useful in such situations. This project, which builds on previous SaferSim automated vehicle research, will utilize driving simulation to examine how driver state information can be used in the context of highly-automated driving. Specifically, we will investigate whether situational awareness can be inferred from state monitoring data and the time needed to regain situational awareness and effectively take control of the vehicle for drivers in impaired states. Differences in response time and transfer of control as a function of driver state would indicate the need for alternate or additional interventions prior to the emergence of critical events and the results of this research will help identify potential intervention strategies.