## 2 Related Work  
There are two types of blind spot detection systems. The first type is radar sensors developed by Robert Bosch GmbH, Germany, as shown in Figure 1[6; 7]. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Levels** & **Description** \\ \hline Level 0 No Automation & The driver fully takes responsibility to operate the vehicle. \\ \hline Level 1 Driver Assistance & The driver operates the vehicle with some driving assistance features included in the vehicle. \\ \hline Level 2 Partial Automation & Automated features such as auto-steering, auto-acceleration, etc. need to be installed in the vehicle, and the driver needs to engage in driving the vehicle and monitoring the environment. \\ \hline Level 3 Conditional Automation & The driver is not required to monitor the environment but needs to control the vehicle when necessary. \\ \hline Level 4 High Automation & The driver is required to optionally control the vehicle, and all the driving functions in the vehicle are operated under certain conditions. \\ \hline Level 5 Full Automation & The driver is required to optionally control the vehicle, and all the driving functions in the vehicle are operated under all conditions. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Six levels of the autonomous driving technology. Figure 1: Radar module of the blind spot detection system. Electromagnetic wave-based two radar sensors are mounted in the rear bumper, and each sensor measures a distance between a radar-equipped vehicle and an approaching vehicle [8]. This type of the system gives visual alarm to the driver when the approaching vehicle is either very close to the radar-equipped vehicle or in the blind spot. This system also gives audio alarm to the driver when the radar-equipped vehicle changes a lane while the approaching vehicle is in the blind spot [8]. The main issue of this system is that it does not detect very fast approaching vehicles and motorcycles well [8]. The second type is the camera-based blind spot detection system. One of the Japanese automobile companies introduced this system in 2013. One camera is mounted underneath the side mirror on the passenger side. This camera is activated when the right turn signal is on or a driver pushes the button on the turn signal switch [9]. Figure 2 shows how this system works. However, this camera-based system also has several issues: (i) the system provides a guideline to see a distance between the system-equipped vehicle and target vehicle in the next lane through the screen, but it does not give any visual or audible warning to drivers, unlike the radar-based blind spot system; and (ii) the mounted camera is very vulnerable to rainwater and contaminants. For this reason, many studies have been conducted to provide the reliable camera-based blind spot detection system. The work in [10] proposed a vision-based blind spot detection system based on optical flow techniques. This system is composed of four computational steps: (i) image processing; (ii) pixel-wise clustering; (iii) analysis of clusters; and (iv) pattern recognition. The image processing step is associated with separating relevant features from the environment due to the expectation of reducing computational time. In this step, the Canny edge extractor is applied to the original incoming image and pixels with a positive value are used for calculating optical flow. In the pixel-wise clustering pixel, Canny edge pixels are grouped together to detect the clusters of pixels. Each cluster of pixels is a candidate for vehicle objects in the processed image. To determine groups of pixels and their likelihood to form a single object, classical clustering techniques are used. Note that this step is the first sub-step in the clustering stage, and the second sub-step is to group or merge two or more clusters into a single cluster based on distance criteria. The reason for splitting this step into two sub-stages is due to the possibility that two objects could be positioned close together.