## 3 Methods to Address the Pipeline of End-To-End ADS
### Image Pre-Processing and Instance Segmentation  
[PERSON] et al. [18] proposed a CNN model for object localization using camera and LiDAR data for 3D multi-view object detection, or MV3D. In this method, three CNNs are deployed in parallel. Two of them are fed with the point cloud LiDAR data (one obtained from a BEV and the other from the front view). The third CNN uses raw RGB images from a camera located at the front, on the sides, and the car's back for a 360-degree view. Once each architecture finishes, a deconvolution process occurs to identify where the tracks are located and their shapes. Like an RCNN architecture, a decoder-like function outputs decoding fed to the architecture performing the localization task and shared between the other parallel architectures. This process generates a 3D object proposal of the classes, combining a region-wise feature obtained via ROI pooling caused by each architecture. The output is a 3D bounding box around the recognized objects. It is important to note that this method can realize the item's class, position, and volume. This method performs with a 14.9% higher accuracy than state-of-the-art methods, comparable in runtime. As a result, this method is computationally demanding, and deducting the 3D bounding box will require a specific and robust hardware architecture for the algorithms to be deployed correctly and in real-time scenarios. [PERSON] et al. [50] proposed VoxelNet, an end-to-end trainable deep architecture for point cloud-based 3D detection. In contrast to the previous two methods, this is a LiDAR-only based detection, which relies on the object size, occlusion state, and other superficial information. To improve the localization and detection, the authors proposed the Multimodal Voxel for 3D Object Detection Network (MVX-Net) framework. In this method, fused augmented LiDAR point clouds with semantic image features are used at early stages to improve 3D object detection. Moreover, two fusion techniques were developed to extend the VoxelNet. The first one is PointFusion, which combines features at early stages to add 3D points to an image feature and capture dense context, extracting features from a 2D detector (i.e., a camera) and then jointly inputting them into the VoxelNet model. The second technique is called VoxelFusion and employs non-empty 3D voxels to be projected on the image plane. Features are extracted and fed into the voxel feature encoder used by a 3D RPN to deduce the bounding boxes. Authors state that the VoxelFusion technique delivers a slightly inferior performance compared to the PointFusion one; however, it has more efficiency in memory consumption. The authors evaluated the KITTI 3D object detection benchmark model and concluded that their approach demonstrated better results than the single modality VoxelNet [51]. Avoiding person-to-person contact is the best way to control and prevent the virus' transmission amidst the COVID-19 epidemic that affected the world in 2020. Still, people require food and other basic goods to address their essential needs. [PERSON] et al. [52] developed Hercules, an autonomous logistic vehicle used for contactless delivery, to address this issue. It has an autonomous navigation capability and a stable hardware-software system to manage the operations. Its maximum payload and capacity are 1000 kg and 3 m\({}^{3}\), sufficient for most of the people delivery's requirements, and uses 3D object detection with LiDAR point clouds to recognize and classify objects. Multiple calibrated LiDARs provide data that are fused as input into the 3D object detector of the VoxelNet. Authors consider that the real-time performance deserved much more attention than accuracy for this particular application. To improve efficiency, they replaced dense convolutional layers with spatially sparse convolutions to obtain inference time boosting. Another critical part of Hercules' perception task was to build the 3D map of the environment with 3D point clouds from the LiDARs and the readings from the inertial measurement unit.