# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 3 Results
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The Encoders Only configuration presents a mean of 0.46 m and a median of 0.42 m, reflecting intermediate performance but still less accurate than the combination of RTK and encoders. In terms of variability, Figure 14 shows significantly less dispersion in the RTK + Encoders configuration, with a standard deviation of 0.14 m and few outliers. This contrasts with the No Sensors configuration, which exhibits wide dispersion and multiple outliers, reaching a maximum of 1.75 m, with a standard deviation of 0.42 m. The Encoders Only configuration also exhibits some dispersion, with outliers reaching up to 1.32 m and a standard deviation of 0.28 m. This indicates that although encoders improve accuracy compared to the total absence of sensors, they still introduce some variability. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Trajectory** & **Min (m)** & **Max (m)** & **Mean (m)** & **Median (m)** & **Standard Deviation (m)** & **RMS (m)** \\ \hline RTK + Encoders & 0.00 & 0.75 & 0.20 & 0.17 & 0.14 & 0.25 \\ No Sensors & 0.00 & 1.75 & 0.45 & 0.31 & 0.42 & 0.61 \\ Encoders Only & 0.00 & 1.32 & 0.46 & 0.42 & 0.28 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Deviation metrics for different sensor configurations. Figure 14: Comparison of deviation distribution across sensor configurations. The agreement between Figure 14 and Table 7 confirms that the RTK + Encoders configuration is optimal for tasks requiring high accuracy in trajectory tracking, particularly in the context of RPSCs designed for road construction projects. Sensor integration enables the correction of residual errors and maintains consistency under conditions typical of roadwork environments, where environmental disturbances and irregular movement can significantly impact accuracy. To support this claim with robust statistical evidence, a comprehensive hypothesis testing analysis was conducted, as shown in Table 8. First, the normality of the distributions was evaluated using the Shapiro-Wilk test. The results indicated that none of the assessed configurations followed a normal distribution (W = 0.7750 for RTK + Encoders, W = 0.9522 for Encoders Only, and W = 0.8165 for No Sensors, all with \(p<0.000001\)). This deviation from normality rules out the use of parametric methods such as ANOVA, due to the non-Gaussian nature of the data. The lack of normality can be attributed to outliers and skewed distributions caused by real-world testing conditions. Subsequently, [PERSON]'s test was applied to assess the homogeneity of variances across groups. The results (W = 79.7068, \(p<0.000001\)) indicated significant differences in data dispersion between the configurations. Since neither normality nor homogeneity of variances was satisfied, non-parametric tests were used. The Kruskal-Wallis test, appropriate under these conditions, revealed significant differences between the medians of the three configurations (H = 166.45, \(p<0.000001\)), confirming that at least one configuration performs differently from the others. To precisely identify these differences, pairwise comparisons were conducted using the Mann-Whitney U test, with Bonferroni correction applied to control for multiple comparisons. The results were as follows:  
* RTK + Encoders vs. No Sensors: U = 32,807, adjusted \(p<0.000001\);
* RTK + Encoders vs. Encoders Only: U = 17,023, adjusted \(p<0.000001\);
* Encoders Only vs. No Sensors: U = 34,401, adjusted \(p=0.013\). These results indicate that the RTK + Encoders configuration delivers significantly superior performance. Moreover, while the Encoders Only setup slightly improves the No Sensors configuration, the difference is also statistically significant, albeit smaller. This