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Fast and credible identification and estimation of driver's stress-level and stress-type from sensed physiological signals has been one of the critical research areas in the recent past. Several good metrics and mechanisms involving bioelectric signals like the Galvanic Skin Response (GSR), Electrocardiogram (ECG) and the Photoplethysmography (PPG) have been identified by the scholars over the years. This paper discusses the features extracted from physiological data collected in five different scenarios and their usefulness with the help of statistical trend analysis methods. The algorithm developed comprises of a novel shape-based feature weight allocation approach and a technique for credible online realtime stress-trend detection. Such a stress-trend detection by the mesh of embedded sensory elements residing in the e-fabric of a wearable computing system will help in reducing chances of fatal driving errors by the way of in-time activation of alerts and actuation of corresponding safety / recovery procedures.