## 5 Conclusions  
This paper presents a method for detecting objects and issuing collision warnings for blind-spot detection and lane change assistance using a rearview camera. Lane detection was performed to ascertain the vehicle's location, and a technique was employed to compensate for vertical vibrations induced by vehicle motion to delineate the area for blind-spot detection and assess the time to collision. The proposed lane detection methodology extracts lane markings by applying edge pairs derived from edge detection results, conducting third-degree polynomial fitting to identify lanes, and enhancing lane detection performance by leveraging historical lane data through tracking mechanisms. The vertical vibrations caused by vehicle motion are compensated for by utilizing the vanishing point at which the two lanes converge. Furthermore, object detection was performed using YOLOv9, resulting in a precision of  
Figure 18: Collision warning results (Green box is lv.1, yellow box is lv.2 and red box is lv.3): (**a**) motorcycle collision warning on urban road, (**b**) car collision warning on urban road, and (**c**) truck collision warning on highway. \begin{table}
\begin{tabular}{l c c c} \hline  & **Mean** & **Variance** & **Standard Deviation** \\ \hline Without vanishing point & 0.3354 & 0.0657 & 0.2563 \\ Using vanishing point & 0.1863 & 0.0119 & 0.1093 \\ \hline \end{tabular}
\end{table} 
Table 12: Comparison of relative distance with and without vanishing point. 90.2% and a recall of 82.8%. The relative speed was calculated from the relative distance using object tracking information. The collision time was then determined using the relative distance and relative speed. A three-level collision warning system was implemented based on collision time and relative distance. The proposed method demonstrated a performance of 11.64 fps with an execution speed of 85.87 ms. Through this three-level collision warning method, the system provides real-time side and rear collision warnings to drivers and autonomous vehicles when changing lanes, functioning as part of a blind-spot detection and lane change assistance system. Moreover, the system effectively issues collision risk warnings for various objects through a three-level collision warning framework that adapts to the relative speed of the obstacle. In future work, we aim to expand on this research to develop a system that operates not only during the daytime but also at night by performing object detection and lane detection in nighttime conditions using a rearview camera. Additionally, we will explore how to ensure system functionality in various adverse weather conditions, such as rain or snow, by acquiring relevant data in these challenging environments. Furthermore, we have enhanced the detection of omnidirectional objects by implementing an around-view monitoring system, which employs four cameras strategically positioned to cover all vehicle directions. This advancement is expected to improve performance in situations where obstacle detection is challenging. Since the around-view monitoring system must detect all surrounding obstacles, we plan to extend our study to include object detection not only for cars, motorcycles, trucks, and buses but also for pedestrians and bicycles. To compare performance, we will investigate and evaluate the effectiveness of radar and lidar by installing these distance sensors on the experimental vehicle, allowing for a direct comparison of their performance. Additionally, we intend to focus on optimizing deep learning algorithms for deployment in devices equipped with on-device artificial intelligence (AI). In the future, we aim to harness on-device AI for applications in autonomous vehicles, particularly for facilitating lane-changing maneuvers. [PERSON] developed the algorithm and performed the experiments. [PERSON] contributed to the validation of the experiments and research supervision. [PERSON] contributed to the manuscript writing. [PERSON] contributed to the review and edited the manuscript.