The paper talks about Quantification of Alertness for vision based Driver Drowsiness and Alertness Warning System (DDAWS). The quantification of alertness, as per Karolinska Sleepiness Scale (KSS), reads the basic input of facial features & behaviour recognition of driver in a standard manner. Although quantification of alertness is inconclusive with respect to the true value, the paper emphasised on systematic validation process of the system covering various scenarios in order to evaluate the system’s functionality very close to the reality. The methodology depends on definition of threshold values of blink and head pose. The facial features are defined by number of blinks with classification of heavy blink and light blink and head pose in (x, y, z) directions. The Human Machine Interface (HMI) warnings are selected in the form of visual and acoustic signals. Frequency, Amplitude and Illumination of HMI alerts are specified. The protocols and trigger functions are defined and KSS stage is calculated for selected duration of time and frames of data covering demographics of people, road types, weather conditions and human behavioural actions. Multiple iterations of threshold values are conducted for test, the outcomes are listed and results are analysed. In order to enhance the system’s robustness and reliability of the obtained results, the paper added provision of secondary strategies based on vehicle metrics like Standard Lane Deviation Laterally (SDLAT), Yaw rate, latest activation of controls, reversal steering correction rate etc. The paper concludes with the threshold value of eye blink, HMI warning, results of hybrid approach of driver drowsiness and alertness warning system and discussed the scope of improvement.