Today's vehicles are being more often equipped with systems, which are autonomously influencing the vehicle behavior. The close future is awaiting more systems of the kind and even significant penetration of fully autonomous vehicles in regular traffic is expected by OEMs in Europe around year 2025. The driving is highly multitasking activity and human errors emerge in situations, when he is not able 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 similar or higher amount of information, than driver uses in his decision making process. Therefore, currently used, and debatable, 1-D quantity TTC (time-to-collision) will definitely 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 also need to know, if the driver’s situation awareness matches at least the desired threshold. Such knowledge can be provided 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 necessary braking time, but mitigation by changing direction is easily feasible. Opposed to TTC, considering time as an input can be highly appreciated i.e. when switching from autonomous to manual driving mode. For such situation we can observe i.e. two kinds of reaction spaces – one, tightly connected with the requirements of autopilot, and second, resulting from the expected human reaction. Effects of entropy in 2-D reaction space and its application will be presented in the paper.