## 1 Introduction  
With the automobile industry currently experiencing growth due to an increase in the number of vehicles, research on autonomous vehicles is being actively pursued. Driver assistance systems (DASs) for autonomous vehicles are also a significant area of intensive research because they play a vital role in the functionality of these vehicles. Systems designed to prevent accidents--crucial for ensuring safety--have been studied in various fields. Systems designed to prevent car accidents include lane-keeping assistance, forward collision warnings, autonomous emergency braking, blind-spot warnings, and automatic parking [1, 2]. In particular, the blind-spot collision warning system and lane change assistance system are designed to detect objects to the sides and at the rear of a vehicle, alert the driver to potential collision risks from the side or rear, or relay this information to the autonomous vehicle. This system is particularly important for ensuring safe lane changes [3; 4].  
The blind-spot collision warning and lane change assistance system consists of two main components: one for detecting rear objects and the other for assessing the risk of collision with those objects. The rear-object detection component utilizes various sensors integrated into a vehicle, broadly categorized into radar- and camera-based methods [5; 6; 7; 8]. The radar method involves mounting radar sensors on both sides of a vehicle's rear bumper to detect objects. In contrast, camera-based methods involve installing a camera under a side mirror and employing a rearview camera. The camera-based approach offers the advantage of distinguishing between various object types, enabling the delivery of customized warnings based on the specific object type. Furthermore, cross-entropy methods are occasionally employed to evaluate the collision risk associated with detected objects [9]. The collision risk assessment component leverages data from cameras or radar sensors to differentiate between approaching and receding objects and provides warnings regarding objects in the approaching direction when they enter a designated area or when the time to collision is within a specified time range.  
In this study, a rearview camera was used to detect the rear objects. While two sensors are required when installing existing radar systems or side-mirror cameras, using a rear camera offers the advantage of real-time implementation. When two cameras are used, two images must be processed simultaneously, but with a single camera, only one image is processed, allowing for faster processing speeds. In addition, considering that the rear camera is typically installed by default, extra sensors are not required, thus allowing the rear camera to be effectively used in parking situations. Figure 1 shows a block diagram of the proposed method.  
As shown in Figure 1, lane detection was performed using a rearview camera when an input image was received. This approach accurately determines the locations of side and rear vehicles based on lane detection results. The detected lanes establish a warning area, with side and rear obstacles identified using a convolutional neural network (CNN). The detected obstacles are then tracked, and the system calculates the collision risk for these tracked objects, with collision risk assessment categorized into three levels to warn the driver. The results of the collision risk assessment assist with lane changes by preventing autonomous vehicles from changing lanes when necessary.  
The remainder of this paper is organized as follows: Section 2 reviews related studies on blind-spot detection and lane change assistance systems, Section 3 presents the proposed method, Section 4 discusses the experimental results, and Section 5 concludes the paper.  
Figure 1: Proposed blind-spot detection and lane change assistance system using CNN and lane detection.