Vision-based modules are largely exploited by autonomous driving vehicles to identify the road area and to avoid collisions with other vehicles, pedestrians and obstacles. This paper illustrates the results of a comparative study in which eight different vision-based modules are evaluated for detecting free navigational space in urban environments. All modules are implemented using Convolutions Neural Networks. The distinctive and innovative feature of these modules is the manner via which navigational freespace is identified from image inputs. The modules generate the coordinates of a triangle, whose area represents the navigation freespace. The relative position of the triangle top corner with respect to the image centre points toward the vehicle direction of motion. Thus, when trained on a fixed route, these modules are able to successfully detect the road-freepsace and to make appropriate decisions concerning where to go at roundabouts, intersections etc., in order to reach the final destination.