As autonomous and semiautonomous systems are developed for automotive, aviation, cyber, robotics and other applications, the ability of human operators to effectively oversee and interact with them when needed poses a significant challenge. An automation conundrum exists in which as more autonomy is added to a system, and its reliability and robustness increase, the lower the situation awareness of human operators and the less likely that they will be able to take over manual control when needed. Here we define “the system” as consisting of the platform, the software and the human “operator”. Today’s system autonomy efforts are leveraging computational intelligence and learning algorithms to better adapt the platforms response to unanticipated and changing situations. These approaches generally do not consider the human operator’s role as a critical component of the autonomous system. Unexpected automation transitions will occur when the automation suddenly passes control to the human operator who cognitively may not be ready to take over. The general mathematical structure of quantum theory is not only applicable to physics but to any scientific domain that has a need to formalize uncertainty and probably and can be formally applied to human cognition. It can be used, for example, to determine, in real time, the mental state of the human operator. These states for example may include, drowsiness, fatigue and distraction. We propose to use modern, non-invasive brain wave monitoring technology and quantum theory to determine, in real time, the mental state of human operators in autonomous self-driving vehicles. This will provide the vehicle with the needed situational awareness of the human in order to mitigate the existing automation conundrum.