## 5 Conclusions  
V2V positioning is a fundamental part of any intelligent transport system, such as an autonomous vehicle, because it helps to prevent traffic accidents and congestion, especially in nighttime situations. The stereo vision system is promising as a potential solution, owing to its existing advantages compared to other existing technologies. This paper considers a stereo-vision-based nighttime V2V positioning process which is based on detecting the vehicle taillights and using the stereo triangulation approach. However, in the urban traffic environment, each camera in the system often captures multiple light  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Correct Pairing** & **False Pairing** & **Missed Pairing** & **Precision (\%)** & **Recall (\%)** & **F-Score (\%)** \\ \hline
3618 & 386 & 196 & 90.38 & 94.87 & 92.57 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Taillight pairing results.  
Figure 27: Building dataset for constructing and evaluating pairingClassifier.  
sources in a single image, which leads to three problems for the V2V positioning task: the detection problem, the stereo matching problem, and the pairing problem. To address these problems, in this study, a threefold contribution is proposed. The first contribution is a detection method for labeling and determining the pixel coordinates of each taillight region in the image. Second, we propose a stereo taillight matching method using a gradient boosted tree classifier, which helps in determining which taillight detected in the left image corresponds to a taillight detected in the right image while ignoring the remaining parts of the images, which are redundant for the V2V positioning task. Third, a neural-network-based pairing method is provided to associate every two taillights that belong to the same vehicle. We also conducted an outdoor experiment to evaluate the performance of each proposed method in urban traffic at nighttime. In future work, it is necessary to quantitatively compare the performance of each proposed method with the existing alternatives in different aspects to show their potential to be utilized in autonomous vehicles.  
There are two main directions to develop this study for future research. The first direction is to improve the explainability and performance of each proposed method, especially the taillight detection method, as the two remaining ones greatly depend on the detection result. To fulfill this target, some advanced deep learning approaches, such as faster-RCNN, can be considered to provide a better solution. The second direction is to expand the research scope into detecting vehicle headlights, daytime positioning, or dealing with several weather conditions. All these directions aim to provide a robust V2V positioning system that can work efficiently in different circumstances of urban traffic.  
[PERSON]-[PERSON] proposed the idea, performed the analysis, and wrote the manuscript. [PERSON] provided the guidance for data analysis and paper writing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (NRF-2018R1A2B6004371).  
The authors declare no conflict of interest.