Today's vehicles are being more often equipped with systems, which are autonomously influencing the vehicle behavior. More systems of the kind and even fully autonomous vehicles in regular traffic are expected by OEMs in Europe around year 2025. Driving is highly multitasking activity and human errors emerge in situations, when he is unable to process and understand the essential amount of information. Future autonomous systems very often rely on some type of inter-vehicular communication. This shall provide the vehicle with higher amount of information, than driver uses in his decision making process. Therefore, currently used 1-D quantity TTC (time-to-collision) will become inadequate. Regardless the vehicle is driven by human or robot, it’s always necessary to know, whether and which reaction is necessary to perform. Adaptable autonomous vehicle systems will need to analyze the driver’s situation awareness level. Such knowledge can be enhanced by 2-D quantity, so called reaction space, and its entropy. The new approach defines a limit space, where ego vehicle or other vehicles can be present in the future specified by an amount of time. This enables the option of counting not only with braking time, but mitigation by changing direction is feasible. Opposed to TTC, considering time as an input is appreciated especially when switching from autonomous to manual driving. For such situation we observe two kinds of reaction spaces – one, connected with the requirements of autopilot, and second, resulting from the expected human reaction. Effects of entropy in 2-D reaction space are presented in the paper.