## 1 Introduction
From the perspective of training, when networks are separately trained and inferred for segmentation and detection, time and resources are wasted in an autonomous driving system with limited computing resources. Namely, both semantic segmentation and object detection are very important roles for autonomous vehicle systems but need very large computational resources. In many previous studies, semantic segmentation to which Fully Convolutional Network(FCN) [13] and U-Net [14] structures are applied has been performed based on images obtained using a camera, or object detection using Faster-RCNN [15], RetinaNet [16], or YOLOv3 [17] networks, with FCN modifying models trained for existing image classification in order to make them appropriate for the purpose of semantic segmentation. FCN is able to have spatial information by replacing a fully connected layer with a full-convolution layer, thus overcoming the limitations of a fully connected layer and influencing many subsequent semantic segmentation-related studies. U-Net uses the extended decoding method from FCN, U-Net proposed a semantic segmentation model in which encoders and decoders have symmetrical structures in the form of 'U' characters. Through the structure of connecting layers corresponding to each other in the encoder and decoder, more accurate localization is possible while maintaining semantic information. Faster-RCNN is a two-stage object detector that combines RPN, suggesting where the object might exist, and a detector that classifies the type of object and predicts the location of object. RetinaNet proposes a focal loss that gives small weights for well-classified classes and large weights for classes that are difficult to classify, so that all classes are well classified. YOLOv3 is a one-stage object detector that improves the existing YOLO and has better detection performance than other detectors. However, these image-based approaches have the limitations mentioned above, and only one semantic segmentation or object detection task is targeted. Furthermore, studies are being conducted on semantic segmentation such as RangeNet++ [18] and PointSeg [19] using a point cloud obtained by LiDAR and on object detection by PIXOR [20] and MV3D [21]. RangeNet++ is a method for performing rapid semantic segmentation by performing a spherical projection method for a 3D point cloud. PointSeg uses the light-weight network SqueezeNetto improve speed and model accuracy. PIXOR performs object detection at high speed by reducing dimension by means of a top view projection of a 3D point cloud. MV3D is a method for performing object detection by fusing the top view projection, front view projection, and camera image of a 3D point cloud as input. However, these are also methods for either segmentation or detection, making them unsuitable for an autonomous driving system. Therefore, we introduce a SSADNet based on LiDAR that is stronger in terms of reliability than cameras for recognition, and integrates semantic segmentation and object detection into a single architecture. In this paper, therefore, we introduce a method for performing segmentation and detection simultaneously for an autonomous driving system that uses LiDAR. This method can be applied to any type of environment without distortion to ensure the safety and reliability of the decisions and control modules that need to be processed in real world implementation using limited computing resources. The proposed network aims to be more favorable for real-world processing (which is necessary for an autonomous driving system) and the reasonable performance of segmentation and detection. While previous networks require more than one processor for each network, using more than one independent network to perform two functions, the proposed algorithm allows two functions to be performed simultaneously in one network structure, with fewer processors than conventional algorithms.