## 1 Introduction  
An autonomous driving system involves recognizing the driving environment using sensors installed on a vehicle and deciding an optimal path for driving the vehicle autonomously. In general, autonomous driving systems consist of three modules for perception, decision, and control, and each module consists of algorithms corresponding to the role of a driver. The perception module recognizes the environment around the vehicle and evades static or dynamic obstacles using sensors installed on a vehicle while recognizing circumstances such as the location of a vehicle [1]. The decision module decides on driving strategies such as passing, turning, accelerating, or decelerating based on the recognized environment and determines the driving path to evade obstacles while driving on the target path. The control module controls the actuator to the targeted steering angle and vehicle speed according to the values determined by the judgment module [2]. The three modules of perception, decision, and control are closely related to each other in an autonomous driving system. In particular, the decision module and control module must have accurate perception to ensure safety and reliability [3]. Recently, there have been many studies on deep learning-based semantic segmentation and object detection using the data obtained from environment recognition sensors as input for the perception of an autonomous driving system for many-sided purposes in various environments [4; 5; 6]. Cameras and LiDAR are the most widely used sensors for recognizing the driving environment in an autonomous driving system. A camera is useful for recognizing the types of static and dynamic obstacles that a vehicle may encounter while driving. However, a camera is easily affected by environmental factors such as weather and intensity of illumination in which distortion may occur due to the characteristics of the camera lens. On the other hand, LiDAR is less affected by environmental factors than a camera, and it can obtain information on the environment around a vehicle through omnidirectional scanning without distortion. In general, moreover, LiDAR provides more accurate distance information than a camera and shows better environmental robustness than a camera. The most widely used deep learning-based perception methods for autonomous vehicles include semantic segmentation and object detection. First of all, semantic segmentation [7] is a process of classifying each pixel of an image into a distinct class label. Many sensors, such as camera or LiDAR, have been used for deep learning-based semantic segmentation [8; 9]. Meanwhile, object detection [10] refers to classifying the class of each object in input images having multiple objects and even finding the location of those objects. To find the location, a bounding box is used in a regression analysis for prediction. Recently, research has been conducted on object detection using radar and camera [11; 12]. A class corresponding to a certain bounding box can be identified through semantic segmentation, and the type of a specific object within a bounding box can be identified through object detection. Semantic segmentation and object detection are similar in the sense that the type and location of multiple objects can be identified. However, semantic segmentation has the advantage of being able to find specific areas within a space (such as drivable areas) by classifying them in a pixelwise manner, while object detection can find specific objects such as vehicles or pedestrians since the type of an object can be identified within a bounding box. Therefore, semantic segmentation and object detection use different methods depending on the target and purpose of recognition; thus, both processes are required for the perception in an autonomous driving system. In most studies, however, semantic segmentation and object detection have been considered as separate tasks.