We seek to improve vehicle automation by using the state of the driver to develop an adaptive assistance system. We focus on the problem of measuring the driver state under varying levels of cognitive workload using affective (i.e. emotion) sensing, including thermal facial analysis and electroencephalography (EEG). This information is then used in sensor fusion and machine learning algorithms to help predict the brake reaction time of the driver, a key input in forward collision warning systems. We demonstrate the results in a pilot study, which highlights the benefits of the personalized, adaptive reaction time estimation in collision warning alert performance. A 40-50% improvement in alert precision is observed with the adaptive approach. We conclude with improvements to further strengthen the quality of the reaction time estimation and improve alert performance.