With the increasing integration of Automated Vehicles (AVs) into our daily traffic, validating their performance poses a significant challenge. Virtual testing, where simulated AVs operate in a simulated environment, has become a widely adopted approach for efficient and cost-effective validation. However, the lack of realistic human behaviour models hinders the realism of these simulations, particularly in simulating reciprocal driver interactions. In this research, I introduce a discretionary lane change model based on the Communication-Enabled Interaction (CEI) framework, which simulates reciprocal driver interactions through implicit communication and belief modelling. These reciprocal interactions involve mutual behaviours, where individual drivers contribute through high-level decisions and low-level control actions. By employing the CEI framework, decision-making and control actions are integrated into a unified model. The proposed model is validated against naturalistic driving data in discretionary lane change scenarios to assess its validity. Results demonstrate that the model successfully reproduces qualitative and quantitative characteristics of human driving behaviour, reflecting both individual behaviours and the collective contributions of multiple drivers. Moreover, it reflects how varied tactical decisions yield distinct, human-like operational execution characteristics. Thereby improving the realism of interactive traffic simulations and posing a step towards improving virtual testing environments for AVs.