Robust lane recognition for autonomous driving

Abstract

An accurate and robust lane recognition is a key aspect for autonomous cars of the near future. This paper presents the design and implementation of a robust autonomous driving algorithm using the proven Viola-Jones object detection method for lane recognition. The Viola-Jones method is used to detect traffic cones that are located besides the road as it can be done in emergency situations. The positions of the traffic cones are analyzed to provide a model of the road. Based on this model, a vehicle is autonomously and safely driven through the emergency situation. The presented approach is implemented on a raspberry pi and evaluated using a driving simulator. For high resolution images with a size of 1920×1080 pixels, the execution time for object detection is less than 218 ms while a high detection rate is established. Furthermore, the planning and execution for autonomous driving requires only 0.55 ms.