End to End Structural Prediction and Lane Detection for Autonomous Vehicle

Abstract

Automated road lane detection is the part of vision-based driver assistance system of intelligent vehicles. This driver assistance system reduces the road accidents, enhances safety and improves the traffic conditions. In this paper, we present an algorithm for detecting marks of road lane and road boundary with a view to the smart navigation of intelligent vehicles. Initially, the algorithm converts the RGB road scene image into gray image and employs the flood-fill algorithm to label the connected components of that gray image. Afterwards, the largest connected component, which is the road region, is extracted from the labelled image using maximum width and no. of pixels. Eventually, the outside region is subtracted and the marks or road lane and road boundary are extracted from connected components. The numerical results show the effectiveness of the proposed algorithm on both straight and slightly curved road scene images under different day light conditions and the presence of shadows on the roads. Real-time automated road lane detection is an indispensable part of intelligent vehicle safety system. The most significant development for intelligent vehicles is driver assistance system. This driver assistance system holds great promise in increasing safety, convenience and efficiency of driving. The driver assistance system involves camera-assisted system, which takes the real-time images from the surroundings of the vehicle and displays relevant information to the driver. Thus, intelligent vehicles automatically collect the road lane information and vehicle position relative to the lane. Consequently, the system used by the intelligent vehicles provides the means to alert the drivers, which are swerving off the lane without prior use of the blinker. Therefore, intelligent vehicles will clearly enhance traffic safety if they are extensively taken into use