## 2 Related Works  
Sensor technologies for blind-spot detection and lane-change assistance systems can be classified into three categories: radar, cameras, and ultrasonic sensors [3; 4; 5; 6; 10].  
Radar is currently the most widely used method for blind-spot detection and lane change assistance systems. Radar-based detection is implemented by mounting radar sensors on each end of the rear bumper to monitor both the side and rear of the vehicle, thus allowing objects to be detected in these areas [6; 7]. Ultrasonic sensors are mounted on the rear bumper and oriented towards the sides to detect objects on the left and right sides of the vehicle [10].  
Methods that use radar or ultrasonic sensors detect objects but have the disadvantage of being unable to differentiate between object types and are often affected by surrounding elements such as guardrails. In contrast, camera-based systems offer the advantage of distinguishing between different types of objects, such as cars, buses, or trucks.  
There are two primary methods of using cameras to detect rear objects. The first involves mounting a camera under a side mirror to detect objects to the sides and rear. The second uses a rearview camera for object detection [4; 7; 8].  
Various methods for detecting side and rear objects using side cameras are currently being investigated. [PERSON] et al. employed motion detection for object identification, whereas [PERSON] et al. used appearance and edge features [11; 12]. [PERSON] et al. implemented a convolutional neural network for object detection, and [PERSON] et al. applied a neural network for similar purposes [13; 14]. Significant research is also underway on rear-object detection using rear cameras. [PERSON] et al. utilized a Census Transform from a top-view perspective for object detection [15]. [PERSON] et al. applied AdaBoost and Hough circle transforms for a similar purpose [16]. [PERSON] et al. employed a support vector machine [17], whereas [PERSON] et al. converted images into side-rectilinear formats for enhanced detection [4]. [PERSON] et al. applied a side-rectilinear image conversion technique in conjunction with a generative adversarial network (GAN) to improve nighttime object detection [8].  
Mounting a camera under the side mirror requires two cameras because each camera monitors only one side. In contrast, using a rear camera offers the advantage of detecting objects on both sides using a single camera. In addition, a rear camera is already used for low-speed parking assistance; therefore, at high speeds, the same camera could be used for object detection, which negates the need for additional camera/sensor installations, thus allowing multiple systems to be configured with one camera. Although a side-mounted camera can detect vehicles directly adjacent to them, a rear camera cannot detect immediately adjacent vehicles. Nevertheless, a system incorporating a rear camera with an object tracking method can still warn of potential collisions with vehicles.  
Additionally, the rear camera method has the advantage of faster processing speeds compared to the side-mirror camera setup, as it only requires the processing of a single image instead of two. Consequently, the proposed method utilizes a rear camera to enable real-time system configuration.