A major challenge for autonomous vehicles is handling interactive scenarios, such as highway merging, with human-driven vehicles. A better understanding of human interactive behaviour could help address this challenge. Such understanding could be obtained through modelling human behaviour. However, existing modelling approaches predominantly neglect communication between drivers and assume that some drivers in the interaction only respond to others, but do not actively influence them. Here we argue that addressing these two limitations is crucial for accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model the interaction in an integral way rather than modelling an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication and bounded rationality. We demonstrate the model in a simplified merging scenario, illustrating that it generates plausible interactive behaviour (e.g., aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model's decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles.