Study and Implementation of Lane Detection and Lane Keeping for Autonomous Driving Vehicles

Advanced Driving Assistant Systems (ADAS), intelligent and autonomous vehicles have the aim to improve road safety, traffic issues and the comfort of passengers. For this purpose, lane detection and lane keeping (LK) systems are important challenges. This thesis has been focused on the implementation of a model that is able to compute information necessary to develop a lane keeping function for an autonomous driving vehicle. The model is composed by lane detection, trajectory generation and lane keeping control and it has been realized with environment data coming from a camera, that is the most frequently sensor used to implement this type of applications. The overall system has been developed with software as MATLAB and Simulink. In particular, the lane detection has been performed with the Automated Driving System Toolbox that allows to develop and test ADAS and autonomous driving systems providing computer vision algorithms. Starting from the information obtained with the lane detection stage, it has been possible to generate the trajectory necessary for the development of the lane keeping. In this research, the trajectory has been identified by the center line of the detected lane in the road environment. In order to follow the trajectory, it has been realized a lateral controller which is mainly related to the computation of the front wheel steering angle. The controller has been implemented using Model Predictive Control (MPC) theory. Thanks to the overall model, the values of the front wheel steering angle have been computed allowing an autonomous driving vehicle to follow a specific trajectory.