# Selecting Remote Driving Locations for Latency Sensitive Reliable Tele-Operation
## 3 Selecting Driving Locations for Multiple Paths
### Selecting a Fixed Number of Driving Locations for Smallest Distance
\begin{table}
\begin{tabular}{l l} \hline \hline
**Algorithms** & **Analysis** \\ \hline \multirow{4}{*}{LAF ([8])} & LAF (Longest Advance First) tries to select the driving locations for single route. For multiple routes, LAF can be applied multiple times, one run for each route. However, as the results show, applying LAF multiple times does not achieve better results than RDSMP or ERDSMP, which explicitly consider multiple routes. \\ \hline \multirow{4}{*}{RDSMP ([7])} & RDSMP is the first algorithm that tries to select remote driving locations for multiple routes. Since RDSMP tries to reduce the number of remote driving locations with a given latency constraint, it does not explicitly try to reduce the average latency. However, ERDSMP actually tries to reduce both the number of driving locations and the average latency. \\ \hline \multirow{4}{*}{K-Means ([35])} & K-Means algorithm is a well-known algorithm for clustering a given set of points. However, in this remote driving problem, the locations cannot be selected arbitrarily from any positions. Thus, applying K-Means to the remote driving problem may cause some performance penalty so that other more explicit algorithms such as BS perform better than K-Means. \\ \hline \multirow{4}{*}{ERDSMP} & ERDSMP exploits the fact that the initial start of RDSMP can be varied as long as the number of driving locations is the same. Thus, it tries many alternative initial selection of driving locations and choose the best one in reducing the average latency. With some more computation time, which is not a critical performance metric in driving location selection problem, it usually produces better solutions. \\ \hline \multirow{2}{*}{BS} & BS explicitly uses the results of ERDSMP for selecting given number of remote driving locations. Thus, BS can reduce the latency by using ERDSMP and performs efficiently due to the logarithmic nature of binary search. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative Analysis of Algorithms. **Author Contributions:** Conceptualization, S.L. ; methodology, S.Q.Z. and S.L. ; software, S.Q.Z. ; validation, S.Q.Z. and S.L. ; formal analysis, S.Q.Z. and S.L. ; investigation, S.Q.Z. ; resources, S.Q.Z. ; data curation, S.Q.Z. ; writing--original draft preparation, S.Q.Z. ; writing--review and editing, S.Q.Z. and S.L. ; visualization, S.Q.Z. and S.L. ; supervision, S.L. ; project administration, S.Q.Z. and S.L. ; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by Global Scholarship Program for Foreign Graduate Students at Kookmin University in Korea and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1F1A1061136). **Conflicts of Interest:** The authors declare no conflict of interest. ## References

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