Empathic vehicles are a promising concept to increase the safety and acceptance of automated vehicles. However, on the way towards empathic vehicles a lot of research in the area of automated emotion recognition is necessary. Successful methods to detect emotions need to be trained on realistic data that contain the target emotion and come from a setting close to the final application. At the moment, data sets fulfilling these requirements are lacking. Therefore, the goal of this work is to present an experimental paradigm that induces four different emotional states (neutral, positive, frustration and mild anxiety) in a real-world driving setting using a combination of secondary tasks and conversation-based emotional recall. An evaluation of the paradigm using self-report data, annotation of speech data and peripheral physiology indicates that the methods to induce the target emotions were successful. Based on the insights of the experiment, finally a list of recommendations for the induction of emotions in real world driving settings is given.