###### Abstract  
This paper presents a solution for an autonomously driven vehicle (a robotic car) based on artificial intelligence using a supervised learning method. A scaled-down robotic car containing only one camera as a sensor was developed to participate in the RoboCup Portuguese Open Autonomous Driving League competition. This study is based solely on the development of this robotic car, and the results presented are only from this competition. Teams usually solve the competition problem by relying on computer vision algorithms, and no research could be found on neural network model-based assistance for vehicle control. This technique is commonly used in general autonomous driving, and the amount of research is increasing. To train a neural network, a large number of labelled images is necessary; however, these are difficult to obtain. In order to address this problem, a graphical simulator was used with an environment containing the track and the robot/car to extract images for the dataset. A classical computer vision algorithm developed by the authors processes the image data to extract relevant information about the environment and uses it to determine the optimal direction for the vehicle to follow on the track, which is then associated with the respective image-graph. Several trainings were carried out with the created dataset to reach the final neural network model; tests were performed within a simulator, and the effectiveness of the proposed approach was additionally demonstrated through experimental results in two real robotics cars, which performed better than expected. This system proved to be very successful in steering the robotic car on a road-like track, and the agent's performance increased with the use of supervised learning methods. With computer vision algorithms, the system performed an average of 23 complete laps around the track before going off-track, whereas with assistance from the neural network model the system never went off the track. artificial intelligence; AI in engineering; autonomous driving; robotics; simulation; computer vision; neural network; supervised learning +
Footnote †: journal: _Algorithms_  
0000-0001-5800-0000-00001-5800-00001-  
progress in recent years. Most car manufacturers are investing in research for autonomous driving, as it is the next step in modern transportation. There are several levels of autonomy that have been defined by the Society of Automotive Engineers (SAE), ranging from Level 0 (no automation) to Level 5 (full automation) [1]. It is important to mention that most autonomous vehicles are presently able to achieve Level 3 autonomy. The ultimate goal of many companies and researchers working in this field is to achieve Level 5 autonomy, which refers to fully autonomous vehicles. However, reaching this level of autonomy is a very challenging task, and it is difficult to know when it will be achieved. [PERSON] et al. utilized a Convolutional Neural Network (CNN) to predict steering and velocity in autonomous cars [2]. The objective of their study was to replicate human actions; to accomplish this, the Udacity Self-Driving simulator [3] was employed to gather images for the dataset, train the model, and conduct testing. The system takes input from three car cameras to capture images from different angles (left-facing, center-facing, and right-facing). Training was conducted under specific road conditions, while testing was performed under different conditions. The duration for which the car remained within the lane without human intervention was recorded, demonstrating promising results with improved accuracy and a reduced loss rate compared to previous models. However, it is important to note that this solution is insufficient for real-world scenarios due to the increased complexity of actual driving situations involving actions such as reversing, braking, etc. Moreover, the model does not account for obstacles such as pedestrians or other vehicles.