Although there is currently significant development in active vehicle safety (AVS) systems, the number of accidents, injury severity levels and fatalities has not reduced. In fact, human error, low performance, drowsiness and distraction may account for a majority in all the accident causation. Active safety systems are unaware of the context and driver status, so these systems cannot improve these figures. Therefore, this study proposes a 'context and driver aware' (CDA) AVS system structure as a first step in realizing robust, human-centric and intelligent active safety systems. This work develops, evaluates and combines three sub-modules all employing a Gaussian Mixture Model (GMM)/Universal Background Model (UBM) and likelihood maximization learning scheme: biometric driver identification, maneuver recognition, and distraction detection. The resultant combined system contributes in three areas: (1) robust identification: a speaker recognition system is developed in an audio modality to identify the driver in-vehicle conditions requiring robust operation; (2) narrow the available information space for fusion: maneuver recognition system uses estimated driver identification to prune the selection of models and further restrict search space in a novel distraction detection system; (3) response time and performance: the system quickly produces a prediction of driver's distracted behaviour for possible use in accident prevention/avoidance. Overall system performance of the combined system is evaluated on the UTDrive Corpus, confirming the suitability of the proposed system for critical imminent accident cases with narrow time windows. (C) 2010 Elsevier B.V. All rights reserved.