This paper investigates the use of pupil dilation, head movement and EEG for detecting distraction and cognitive load of drivers while performing secondary tasks in an automotive environment. We tracked pupil dilation from Tobii Pro Glasses 2, head movement from Kinect and EEG from Emotive Insight system. We have analyzed data using Fast Fourier Transform, Continuous Wavelet Transform, and Discrete Wavelet Transform for the full-length signal as well as in windows of 1 second for real-time implementation. We investigated detection of distraction and cognitive load from three different conditions - free driving, driving with lane change, driving with lane change and operating secondary task for each participant in a driving simulator. Our results show that the pupil dilation, head yaw, and EEG can detect the increase in cognitive load due to operation of secondary task within a time buffer of 1 second which can be adapted for real-time implementation. We have also found that FFT of Pupil dilation shows significant categorization of normal and distracted states than the categorization by DWT which contrasts with state of the art methods. Finally, we have proposed an expert system to alert drivers utilizing the signal processing analysis.