The number of road accidents is increasing all over the world. When Vulnerable Road Users (VRU) are involved in an accident, they are prone to more serious injuries. Half of the fatalities around the world involve VRUs. One way of mitigating the severity and the number of VRU accidents is the introduction of Advanced Driver Assistance Systems (ADAS) functionalities. Such systems must be validated before being introduced on the market. Real-life tests for validating the system would imply driving around for millions of operational hours which would be very time consuming and therefore expensive. For this purpose, TNO has developed a scenario generation method and is currently extending it. The objective of this study was to develop a vehicle-cyclist interaction model which would then be used along the existing scenario generation method or for validating generated scenarios. The preceding literature study to this Master Thesis, from which the architecture of the models was developed and potential input parameters were defined, was used as a starting point. Based on the architecture, the interaction model was first developed with the help of a Hidden Markov Model (HMM). First, a sensitivity analysis was performed from which it was concluded that speed, acceleration, and steering angle are important parameters for a cyclist, whereas speed acceleration and yaw rate are important for a vehicle’s intent recognition. Furthermore, this analysis also indicated that distance to the center of the crossing is a very good interaction parameter. After selection of the parameters, the model was trained and cross-validated using data earlier recorded at TNO. The cross-validation results of the interaction model showed that the intent recognition is more constant for the whole observation sequence and an improved performance is seen for a prediction time of more than 2s compared to a HMM without interaction parameters. From this study, the kinematic input parameters for the cyclist and the vehicle, as well as the interaction parameters, were defined that need to be included in the vehicle-cyclist interaction model. However, the model developed in this study needs to be further validated using naturalistic vehicle-cyclist kinematic data, and for all type of vehicle-cyclist scenarios.