\begin{tabular}{c | c | c | c | c | c | c | c}
{\bf Method} & {\bf MaxF} & {\bf AP} & {\bf PRE} & {\bf REC} & {\bf FPR} & {\bf FNR} & {\bf Runtime}\\ \hline
CyberMELD+PLARD \cite{9110871} & 94.44 \% & 88.59 \% & 95.95 \% & 92.97 \% & 0.69 \% & 7.03 \% & 0.18 s / 8 cores \\
CyberMELD \cite{9110871} & 93.56 \% & 88.58 \% & 95.94 \% & 91.30 \% & 0.68 \% & 8.70 \% & 0.05 s / 8 core \\
RoadNet3 \cite{lyu2019road} & 91.47 \% & 91.01 \% & 91.78 \% & 91.17 \% & 1.44 \% & 8.83 \% & 300 ms / GPU \\
RBNet \cite{chen2017rbnet} & 90.54 \% & 82.03 \% & 94.92 \% & 86.56 \% & 0.82 \% & 13.44 \% & 0.18 s / GPU \\
Up-Conv-Poly \cite{Oliveira2016IROS} & 89.88 \% & 87.52 \% & 92.01 \% & 87.84 \% & 1.34 \% & 12.16 \% & 0.08 s / GPU \\
SPRAY \cite{Kuehnl2012ITSC} & 83.42 \% & 86.84 \% & 84.76 \% & 82.12 \% & 2.60 \% & 17.88 \% & 45 ms / \\
SPlane + BL \cite{Einecke2014IV} & 69.63 \% & 73.78 \% & 80.01 \% & 61.63 \% & 2.71 \% & 38.37 \% & 2 s / 1 core \\
SCRFFPFHGSP \cite{Gheorghe2015} & 57.22 \% & 39.34 \% & 41.78 \% & 90.79 \% & 22.28 \% & 9.21 \% & 5 s / 8 cores
\end{tabular}