i-Car: An Intelligent and Interactive Interface for Driver Assistance System

The aim of the present research was to reduce accidents by assisting the driver in various aspects of driving such as lane detection, pedestrian and car detection, driver drowsiness detection and rear view parking assistance. The methodology combines the computer vision techniques with pattern recognition, feature extraction, machine learning, object recognition, human computer interaction and parallel processing in a nutshell. The proposed system provides robust extraction of lane markings in various types and alerts the driver attempting to drift from the lane. It also detects the pedestrians and cars which are at a vulnerable distance to be hit by the vehicle and alarms the driver well ahead of time. The system uses eye closure based decision algorithm to detect driver drowsiness in all conditions and also warns by interactive voice early enough to avoid the accidents. Present research findings outperform the state of the art research like CalTech. Attempts of depth sensing even using Microsoft Kinect could be ignored by the present technology, the iCar.