Driver distraction and drowsiness are leading causes of road accidents worldwide. With new EU regulations mandating to have strong monitoring systems for enhancing road safety, traditional Driver Monitoring Systems (DMS) are typically marred by problems like false alarms, low flexibility, and weak on-the-fly performance. The current work focuses on designing VisionS, an adaptive on-the-fly driver monitoring system that improves distraction and drowsiness detection using computer vision and predictive analytics through data fusion with a physiological-based DMS, PredictS, realized through off-the-shelf Garmin wearable devices. Based on head pose dynamics in combination with behavioral patterns, VisionS provides precise on-the-fly feedback, producing timely alerts to offset unsafe driving behavior.