Driving automation technologies have been gradually penetrating the automotive market. The road infrastructure may be temporarily modified by construction sites, requiring suitable adjustments for the vehicle driving task. This thesis aims to develop the lane keeping task in a simulated diversion scenario induced by roadworks, exploiting vision-based lane detection techniques. The vehicle engaged for the simulation is a 1/10 scaled self-driving car, provided by Bosch for the Bosch Future Mobility Challenge (BFMC). Two specific scenarios have been reproduced: lane change and lane narrowing. Color segmentation, performed by using OpenCV C++ libraries, has been crucial for prioritizing yellow lane markings over white standard ones, and for detecting predefined obstacles.