Driving in stressful and frustrating situations remains a common issue in daily traffic scenarios and has been shown to increase the risk for hazardous and aggressive driving style. Aiming to improve road safety with intelligent systems, frustration has to be detected continuously as well as robust and mitigation strategies must be applied effectively. We conducted a driving simulator experiment to collect a dataset and to validate a driving context related frustration induction method. With this dataset, we developed a bimodal frustration detection for the driving context using a temporal support vector machine. The detection combines drivers‘ visual facial features with heart rate measurements and yields an accuracy of 88.7\,\% (AUC of ROC).