###### Abstract
With the help of 20 cameras and a few more sensors, [PERSON] and [PERSON] [4] proposed the use of a Convolutional Neural Network (CNN) trained with a dataset from real images captured by these cameras. The output of the model is able to control vehicle motion such as steering and speed. The CNN was trained in all sorts of terrain and weather during 10 h of driving by a human being. The system then used these trained data as the basis for testing the CNN in a self-driving manner. The data generated during testing were used to improve the model's training. Detecting the road to be navigated is one of the most relevant tasks in autonomous driving. [PERSON] et al. [5] combined multiple techniques, each specialized in a different characteristic, to achieve the optimal solution through the integration of individual contributions. To represent the road space effectively, their proposed solution merges the outputs of two Deep Learning-based techniques, utilizing adaptations of the ENet model. These adaptations focus on road segmentation and road line detection, respectively. The results demonstrate that the combination of these techniques successfully addresses the shortcomings and limitations of each individual model, resulting in a more dependable road detection outcome. This approach outperforms the results obtained by each technique in isolation. Moreover, with this solution employing multiple techniques, the number of simultaneous algorithms can be increased and these algorithms can be updated or replaced without affecting the final detection outcome, as it is not reliant on any specific algorithm. As a final remark, the authors asserted that their Deep Learning approach showcases superior performance compared to most traditional techniques, although a single Deep Learning network may not be universally capable of handling all scenarios effectively. Using LiDAR techniques instead of cameras, it is possible to detect roads and recognize objects on the path [6; 7]. These sensors provide improved sensing depth, allowing for the construction of a 3D perception system that can provide an increased amount of information for the assessment of road positioning and obstacles. Although a promising technique for the future of Level 5 autonomy, the price of these sensor is very high compared to conventional cameras. Other sensors, such as radars, thermal cameras, and even audio can be used; in these cases, data fusion becomes a core problem. [PERSON] et al. [8] proposed a framework in which data from different sources (thermal, LiDAR, RADAR, GPS, RGB camera, and audio) are fused to aid in the decision level for autonomous driving. A Regional Proposal Network (RPN) and a CNN model are used for object detection and classification, and fusion is achieved through a Hybrid Image Fusion model developed by the authors which involves 2D to 3D conversions based on vector projections and matrix transformations. This framework provides a mechanism and models to create the context of the perceived environment for autonomous vehicles. A more in-depth literature survey of the technologies applied to autonomous vehicles can be found in [9; 10; 11], including analyses of research on environment and pedestrian detection, path planning and motion control, and cybersecurity and networking. As can be seen, the use of neural network models for autonomous driving is not new, and in the case of camera-only systems, model training can be achieved by using a simulator in order to build the image dataset. The Car Learning to Act (CARLA) platform [12] was created to provide developers with support for the development, training, and validation of autonomous systems for urban scenarios. [PERSON] et al. [13] used this platform for dataset generation. The images created by the simulator are realistic and can be used to simulate real-life camera images without the constraint of having to drive a car around. Therefore, a dataset can be easily created with cars, people, obstacles, etc., in motion such the images are not just assorted stills but a sequence of events.