<p>Automobiles are becoming more complicated system considering the safety, environment and user luxury. It is becoming a great challenge considering the complexity of driving and safety in modern automotive world with human interaction systems. The current display systems on the instrument panel, which are used for displaying messages, forces the driver to get away from the road view. To overcome this critical behaviour of the driver windshield is used display the information. This unit is called as Head up Display (HUD), which reduces the duration and frequency of the driver to look/deviate away from the traffic situation/scene. As an advance to the HUD, Augmented Reality (AR) concept has come into play, to overcome the drawbacks of HUD such as the risk of hindering pertinent objects of traffic and phenomena like insight channeling and intellectual capture. In order to validate the HUD content, such as object detection, a deep learning based approach for recognizing and identifying the object types along with predicting and interpreting composite situation is proposed. A Deep Convolutional Neural Network (D CNN) is implemented to identify the object of interest in one evaluation from full image and adding to this performance analysis are carried out on different dataset scenarios. The network is implemented and deployed in lab environment aiming for real-time object detection testing system that is used for testing HUD contents. © 2018, Institute of Advanced Scientific Research, Inc. All rights reserved.</p>
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K. R. Ramakrishnan and Dr. Senthil Kumar T., “Deep learning for identification and validation of objects and data viewed through vehicle windshield in lab environment – a DCNN approach”, Journal of Advanced Research in Dynamical and Control Systems, vol. 10, pp. 880-891, 2018.