# Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition
## 1 Introduction
This is used in numerous systems based on simulators [19] and allows a more human-like structured approach. The disadvantage is the dependency on the use of specific predefined scenarios, as very precise semantic labeling is required. To ensure the correct operation, it is important to validate the final model in real environments, where it is more difficult to perform a reliable semantic labeling procedure. In some cases, it takes a great effort to tag the images with the proper semantics. This is also a relevant problem given the need to have very varied scenarios to train and, thus, obtain a good generalization capacity [20]. In this context, a technique is proposed to automate the segmentation and semantic-labeling process based on the combination of scenarios with chromas and the superposition of images in indoor scenarios. Improvements in the segmentation process with additive noise techniques are also studied. Applying semantic labeling to the elements in a scene is not an easy task. The use of Computer Vision techniques to segment or classify these elements can cause numerous failures. Manual labeling seems unfeasible because it is an excessively hard and tedious process. Our proposal is based on building a scenario based on chroma that facilitates the segmentation of some basic elements, such as the lines or the road itself. For this task, we resort to techniques related to chroma key in an HSV color space. This process is quite easy to automate, and the isolated elements can be used to augment other indoor scenarios, creating new fictitious scenarios. These will then be used to train neural networks that must learn to perform the semantic segmentation of these elements and, then, to learn to drive automatically under the supervision of a human operator. The approach discussed in this project proposes an alternative method to automate vehicle steering training without the need for driving simulators. The use of the chroma scenario allows to perform segmentations with the lowest possible failure rate, so that reliable labeled training datasets can be generated to learn the segmentation of road lines. Using a human driver to apply imitation learning has the advantage that his or her driving provides the equivalent of the ground truth annotation needed to apply supervised machine learning or deep learning algorithms to the input images. This also provides a simple way to evaluate the performance of the trained models, which can be compared to human driving. The proposal is also based on the use of a small-scale vehicle model with a front camera that learns automated driving on a small-sized circuit. To obtain the labels that identify the key elements of the scenes and the associated control actions, it will be essential to have a human operator driving the vehicle and whose driving actions are recorded along the image stream. The system is completed with an external computer and a control device (steering wheel), which allows for obtaining and storing the driving datasets to be used in learning. The computer and the small-scale vehicle communicate through wireless connection. The structure of the document follows the scheme indicated below: the general idea of the proposal is presented in the Introduction; the Related Work section addresses the aspects of interest related to autonomous driving and neural networks; in the Materials and Methods section, the technical details of our proposal and the experimental design are described; later, the Experimental Results, and a Discussion of these results, are addressed; and lastly, the Conclusions summarize the highlights of the proposal.