# Daydirex: Translating Nighttime Scenes towards Daytime Driving Experience at Night
## 6 Conclusions  
In this paper, we propose Daydriex, a processing pipeline that makes nighttime scenes look like daytime scenes in the context of driving on a road. Dark area in nighttime scenes have insufficient information, thereby resulting in poor translation quality even with widely used image-to-image translation models such as CycleGAN. To address the problem, Daydriex employs a processing stage to use street view images as hint information. We present a technique for constructing hint images corresponding to the location of vehicle.  
Figure 15: Box plots of the Likert scale responses for the three cases. Daydriex achieves higher median scores than CycleGAN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hint Image Retrieval** & **Position Matching** & **Perspective Matching** & **Image Merging** & **Image Translation** \\ \hline
710 ms & 9 ms & 2 ms & 0.02 ms & 6 ms (128 \(\times\) 128) \\  & & & & 14 ms (256 \(\times\) 256) \\  & & & & 53 ms (512 \(\times\) 512) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average computation time taken for the main processing stages. Please note that the result for the hint image retrieval stage only includes the time of operations needed to generate a panoramic image without the time taken to download images from the street view service.  
Our evaluation shows that Daydriex achieves better performance than using CycleGAN only in terms of the FID score and user perception.  
We have several topics of future work. First, we will test and compare a range of translation models to generate daytime images in our processing pipeline, which currently uses CycleGAN. Considering the fidelity of translated results and computation time, we can find the best performing model. In addition, it would be possible to design a new translation model specific to the nighttime-daytime image translation task in the proposed pipeline, which we leave as a future work. Second, it is an important future work to develop a prototype system for deployment and conduct field tests. Our study to develop the processing pipeline was based on pre-collected data. However, to deploy a working prototype in actual driving situations, we need to address several other issues such as real-time data acquisition, response time, user interaction, and so on. Third, it is necessary to achieve a certain level of resolution and frame rate for image translation to meet users' quality requirements. This would require high computing power. We will study potential solutions such as cloud/edge-assisted computation and caching to address the problem.  
Conceptualization, E.L. and S.K.; methodology, E.L.; software, E.L.; validation, E.L. and S.K.; investigation, E.L. and S.K.; resources, E.L.; writing--original draft preparation, E.L. and S.K.; writing--review and editing, S.K.; visualization, E.L. and S.K.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.  
This work was in part supported by the National Research Foundation of Korea (NRF) grant (No. 2020R1 IIA3060675). This work was also supported in part by the Education and Research Promotion Program of KOREATECH (2019).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.