## 1 Introduction  
Deep Learning (DL) algorithms in Autonomous Vehicles (AV) have evolved in recent years, with works such as [1, 2] providing important developments in the field. In particular, 2D-based camera object detection has received a lot of attention due to the accurate performance achieved by models such as YOLO [3]. One of the reasons behind the performance of object detection models is the presence of large-scale labeled datasets such as COCO [4] with more than 220,000 labeled images of a total of 330,000 with 1.5 M object instances, 80 object categories and 91'stuff' categories. However, 3D sensors, such as light detection and ranging (LiDAR), have attracted the interest of researchers, but due to the lack of labeled data, 3D-based object detection models have not yet reached the same performance as 2D-based ones, such as YOLO which can process 45 frames per second in its original version and 155 frames per second in a FAST YOLO version in real-time systems using 2D cameras [3]. One of the leading datasets for 3D vehicle detection is KITTI [5], but its main weakness is its relatively small size, which contains around 15.000 images, which cannot be enough to train certain DL models. Recently, another 3D-labeled perception dataset has been published as WAYMO [6], with 2030 video segments yielding a total of 390k frames. Because many frames correspond to the same vehicle in the same video, the WAYMO dataset lacks in variability. In addition, both datasets have been developed in a very similar way, using images and point clouds taken from a vehicle with multiple sensors. This point of view, in addition to being the most used in AV systems, is relatively specific and cannot be used in other tasks that need different angles of vision. DL models need large volumes of data for good performance [7] and the availability of the data can be a critical factor in the results of DL algorithms. The datasets mentioned above have been labeled with much less information than the 2D-based ones and have also been presented with a fixed point of view. However, researchers have routinely selected both datasets due to the cost involved in annotating new 3D labels. The challenges facing the AV industry are widely different with different set-ups and configurations of the sensors, e.g., using cameras and LiDAR placed in the middle of a roundabout or on a bridge or a gantry that points over the road. As these datasets are not enough for new scenarios and the tedious and costly work that has to be done to traditionally build new datasets, a new approach, auto-labeled datasets, has to be adopted. Instead of manually labeling 2D data, commercial tools or works based on Convolutional Neural Networks (CNNs) have been proposed to generate auto-labeled datasets [8; 9; 10]. As CNNs achieves contrasting results in 2D object detection, one of the main ideas that has been proposed is to auto-label datasets. There are works such as [8; 11] in which LiDAR point clouds are projected on the input images, obtaining the object bounding box coordinates and the class as a 3D label as output, achieving 3D auto-generated labels. Despite the auto-labeling process in both works, the datasets have been developed with the same point of view as KITTI and WAYMO, so the bias in the datasets is still a problem. However, as far as we are concerned, it is worth researching whether there is a way to use the models present in the literature to generate datasets with a new approach. Before diving into the idea of creating a novel methodology to generate different auto-labeled datasets, it has been proposed to use DL models to detect objects over images similarly to YOLO to generate new labels. In the KITTI dataset, the level of occlusion of an object is used to determine the difficulty of its classification; officially, KITTI proposes three different categories based on occlusion. The accuracy of the model is evaluated using the F-score metric, in which the values for the three difficulty categories are 0.957 for easy, 0.927 for moderate, and 0.740 for hard.