\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
RoadFormer \cite{li2023roadformer} & 98.15 \% & 95.60 \% & 98.07 \% & 98.23 \% & 2.13 \% & 1.77 \% & 0.07 s / GPU \\
SNE-RoadSeg+ \cite{wang2021sne} & 98.13 \% & 95.52 \% & 98.01 \% & 98.25 \% & 2.19 \% & 1.75 \% & 0.08 s / GPU \\
SNE-RoadSegV2 \cite{Feng2024sne} & 98.10 \% & 95.63 \% & 97.98 \% & 98.22 \% & 2.23 \% & 1.78 \% & 0.03 s / GPU \\
PLB-RD \cite{sun2022pseudo} & 98.05 \% & 95.63 \% & 97.89 \% & 98.21 \% & 2.33 \% & 1.79 \% & 0.46 s / GPU \\
LRDNet+ \cite{lrdnet2022} & 97.98 \% & 94.28 \% & 97.67 \% & 98.29 \% & 2.58 \% & 1.71 \% & 0.01 s / GPU \\
LRDNet (L) \cite{lrdnet2022} & 97.91 \% & 93.96 \% & 97.45 \% & 98.37 \% & 2.83 \% & 1.63 \% & 0.1 s / GPU \\
LRDNet(S) \cite{lrdnet2022} & 97.82 \% & 94.29 \% & 97.39 \% & 98.25 \% & 2.90 \% & 1.75 \% & .009 s / GPU \\
PLARD \cite{chen2019progressive} & 97.77 \% & 95.64 \% & 97.75 \% & 97.79 \% & 2.48 \% & 2.21 \% & 0.16 s / GPU \\
USNet \cite{Chang22Fast} & 97.68 \% & 95.13 \% & 97.28 \% & 98.09 \% & 3.02 \% & 1.91 \% & 0.02 s / GPU \\
SNE-RoadSeg \cite{fan2020sneroadseg} & 97.47 \% & 95.63 \% & 97.32 \% & 97.61 \% & 2.96 \% & 2.39 \% & 0.18 s / GPU \\
DFM-RTFNet \cite{wang2021dynamic} & 97.45 \% & 95.63 \% & 97.33 \% & 97.58 \% & 2.94 \% & 2.42 \% & 0.08 s / GPU \\
RBANet \cite{sun2019reverse} & 97.38 \% & 92.67 \% & 96.70 \% & 98.08 \% & 3.68 \% & 1.92 \% & 0.16 s / GPU \\
CLCFNet \cite{GuYK21} & 97.24 \% & 93.84 \% & 97.99 \% & 96.51 \% & 2.18 \% & 3.49 \% & 0.02 s / GPU \\
LC-CRF \cite{GuZTYK19} & 97.08 \% & 92.06 \% & 96.03 \% & 98.16 \% & 4.46 \% & 1.84 \% & 0.18 s / GPU \\
LidCamNet \cite{1809.07941} & 97.08 \% & 95.51 \% & 97.28 \% & 96.88 \% & 2.98 \% & 3.12 \% & 0.15 s / GPU \\
CLCFNet (LiDAR) \cite{GuYK21} & 96.88 \% & 93.71 \% & 97.85 \% & 95.94 \% & 2.32 \% & 4.06 \% & 0.02 s / GPU \\
NIM-RTFNet \cite{wang2020applying} & 96.79 \% & 95.61 \% & 97.03 \% & 96.54 \% & 3.25 \% & 3.46 \% & 0.05 s / GPU \\
RGB36-Cotrain \cite{CaltagironeEtAl2019} & 96.75 \% & 95.39 \% & 96.84 \% & 96.66 \% & 3.46 \% & 3.34 \% & 0.1 s / 1 core \\
SSLGAN \cite{Han2018Semisupervised} & 96.72 \% & 92.99 \% & 97.05 \% & 96.40 \% & 3.22 \% & 3.60 \% & 700ms / GPU \\
TVFNet \cite{GuZYAK19} & 96.47 \% & 93.16 \% & 97.24 \% & 95.71 \% & 2.98 \% & 4.29 \% & 0.04 s / GPU \\
BJN \cite{s21227623} & 96.29 \% & 93.98 \% & 98.14 \% & 94.52 \% & 1.97 \% & 5.48 \% & 0.02 s / 1 core \\
Hadamard-FCN \cite{Oeljeklaus21} & 96.26 \% & 93.32 \% & 95.63 \% & 96.90 \% & 4.86 \% & 3.10 \% & 0.02 s / GPU \\
StixelNet II \cite{DanLevi2017ICCV} & 96.22 \% & 91.24 \% & 95.13 \% & 97.33 \% & 5.48 \% & 2.67 \% & 1.2 s / 1 core \\
MultiNet \cite{DBLPjournalscorrTeichmannWZCU16} & 96.15 \% & 95.36 \% & 95.79 \% & 96.51 \% & 4.67 \% & 3.49 \% & 0.17 s / GPU \\
HA-DeepLabv3+ \cite{fan2020tmech} & 96.10 \% & 95.03 \% & 95.48 \% & 96.73 \% & 5.03 \% & 3.27 \% & 0.06 s / 1 core \\
RBNet \cite{chen2017rbnet} & 96.06 \% & 93.49 \% & 95.80 \% & 96.31 \% & 4.64 \% & 3.69 \% & 0.18 s / GPU \\
LoDNN \cite{CaltagironeEtAl2016} & 96.05 \% & 95.03 \% & 95.79 \% & 96.31 \% & 4.66 \% & 3.69 \% & 18 ms / GPU \\
TEDNet \cite{10.1007978303115471336} & 95.94 \% & 95.31 \% & 96.21 \% & 95.67 \% & 4.14 \% & 4.33 \% & 0.09 s / GPU \\
RoadNet3 \cite{lyu2019road} & 95.88 \% & 95.46 \% & 96.37 \% & 95.40 \% & 3.95 \% & 4.60 \% & 300 ms / GPU \\
Up-Conv-Poly \cite{Oliveira2016IROS} & 95.52 \% & 92.86 \% & 95.37 \% & 95.67 \% & 5.10 \% & 4.33 \% & 0.08 s / GPU \\
DEEP-DIG \cite{munozbulnesdeep2017} & 95.45 \% & 95.41 \% & 95.49 \% & 95.41 \% & 4.96 \% & 4.59 \% & 0.14 s / GPU \\
OFA Net \cite{zhang2019one} & 95.43 \% & 89.10 \% & 92.78 \% & 98.24 \% & 8.41 \% & 1.76 \% & 0.04 s / GPU \\
CLRD \cite{10.1007978303115471336} & 95.41 \% & 94.83 \% & 95.23 \% & 95.59 \% & 5.26 \% & 4.41 \% & 0.05 s / GPU \\
HID-LS \cite{GuZYK17} & 94.89 \% & 91.46 \% & 95.37 \% & 94.42 \% & 5.04 \% & 5.58 \% & 0.25 s / 1 cores \\
ChipNet \cite{8580596} & 94.87 \% & 91.31 \% & 95.21 \% & 94.53 \% & 5.23 \% & 5.47 \% & 12 ms / GPU \\
DDN \cite{Mohan2014ARXIV} & 94.17 \% & 92.70 \% & 96.73 \% & 91.74 \% & 3.41 \% & 8.26 \% & 2 s / GPU \\
FCN-LC \cite{Mendes2016ICRA} & 94.09 \% & 90.26 \% & 94.05 \% & 94.13 \% & 6.55 \% & 5.87 \% & 0.03 s / \\
ALO-AVG-MM \cite{Reis2019IJCNN2019} & 94.05 \% & 90.96 \% & 94.82 \% & 93.29 \% & 5.60 \% & 6.71 \% & 0.0296 sec / \\
RoadNet-RT \cite{bai2020roadnet} & 93.98 \% & 95.19 \% & 94.47 \% & 93.49 \% & 6.01 \% & 6.51 \% & 8m s / GPU \\
RGB36-Super \cite{CaltagironeEtAl2019} & 93.90 \% & 94.39 \% & 94.70 \% & 93.11 \% & 5.73 \% & 6.89 \% & 0.1 s / 1 core \\
Up-Conv \cite{Oliveira2016IROS} & 93.89 \% & 92.62 \% & 94.57 \% & 93.22 \% & 5.89 \% & 6.78 \% & 0.05 s / GPU \\
HIM \cite{Munoz2010ECCV} & 93.55 \% & 90.38 \% & 94.18 \% & 92.92 \% & 6.31 \% & 7.08 \% & 7 s / >8 cores \\
LidarHisto \cite{7989159} & 93.32 \% & 93.19 \% & 95.39 \% & 91.34 \% & 4.85 \% & 8.66 \% & 0.1 s / 1 core \\
StixelNet \cite{Levi2015BMVC} & 93.26 \% & 87.15 \% & 90.63 \% & 96.06 \% & 10.92 \% & 3.94 \% & 1 s / GPU \\
FTP \cite{Laddha2016IV} & 92.98 \% & 92.89 \% & 91.84 \% & 94.15 \% & 9.20 \% & 5.85 \% & 0.28 s / GPU \\
MixedCRF \cite{Han2017Road} & 92.75 \% & 90.24 \% & 94.03 \% & 91.50 \% & 6.39 \% & 8.50 \% & 6s / 1 core \\
BMCF \cite{wang2016multi} & 92.21 \% & 87.99 \% & 91.55 \% & 92.89 \% & 9.43 \% & 7.11 \% & 2.5 s / 1 core \\
HybridCRF \cite{XIAO2018HybridCRF} & 91.95 \% & 86.44 \% & 94.01 \% & 89.98 \% & 6.30 \% & 10.02 \% & 1.5 s / 1 core \\
PGM-ARS \cite{Passani15IV} & 91.76 \% & 84.80 \% & 88.05 \% & 95.80 \% & 14.30 \% & 4.20 \% & 0.05 s / i74700MQ \\
ProbBoost \cite{Vitor2014ICRAWORK} & 91.36 \% & 84.92 \% & 88.18 \% & 94.78 \% & 13.97 \% & 5.22 \% & 2.5 min / >8 cores \\
NNP \cite{Chen2015NIPS} & 91.34 \% & 88.65 \% & 91.07 \% & 91.60 \% & 9.87 \% & 8.40 \% & 5 s / 4 cores \\
multi-task CNN \cite{Oeljeklaus18} & 91.15 \% & 87.45 \% & 85.08 \% & 98.15 \% & 18.92 \% & 1.85 \% & 25.1 ms / GPU \\
SRF \cite{Xiao2016IJARS} & 90.77 \% & 92.44 \% & 89.35 \% & 92.23 \% & 12.08 \% & 7.77 \% & 0.2 s / 1 core \\
RES3D-Velo \cite{Shinzato2014IV} & 90.60 \% & 85.38 \% & 85.96 \% & 95.78 \% & 17.20 \% & 4.22 \% & 0.36 s / 1 core \\
CB \cite{Mendes2015ARXIV} & 90.55 \% & 85.40 \% & 92.75 \% & 88.45 \% & 7.60 \% & 11.55 \% & 2 s / 1 core \\
MAP \cite{Laddha2016IV} & 89.97 \% & 92.14 \% & 87.47 \% & 92.62 \% & 14.58 \% & 7.38 \% & 0.28s / \\
SPRAY \cite{Kuehnl2012ITSC} & 89.69 \% & 93.84 \% & 89.13 \% & 90.25 \% & 12.10 \% & 9.75 \% & 45 ms / \\
ARSL-AMI \cite{Passani2014ITSC} & 89.56 \% & 82.82 \% & 85.87 \% & 93.59 \% & 16.93 \% & 6.41 \% & 0.05 s / 4 cores \\
FusedCRF \cite{Xiao2015IV} & 89.51 \% & 83.53 \% & 86.64 \% & 92.58 \% & 15.69 \% & 7.42 \% & 2 s / 1 core \\
BM \cite{Wang2014IVWORK} & 89.41 \% & 80.61 \% & 83.43 \% & 96.30 \% & 21.02 \% & 3.70 \% & 2 s / 2 cores \\
HistonBoost \cite{GioIV14} & 88.73 \% & 81.57 \% & 84.49 \% & 93.42 \% & 18.85 \% & 6.58 \% & 2.5 min / >8 cores \\
geo+gpr+crf \cite{doi10.11771729881417717058} & 88.20 \% & 82.33 \% & 85.32 \% & 91.27 \% & 17.26 \% & 8.73 \% & 30 s / 1 core \\
GRES3D+VELO \cite{Shinzato2015} & 88.19 \% & 88.65 \% & 83.98 \% & 92.85 \% & 19.48 \% & 7.15 \% & 60 ms / 4 cores \\
SCRFFPFHGSP \cite{Gheorghe2015} & 87.96 \% & 83.16 \% & 90.01 \% & 86.01 \% & 10.50 \% & 13.99 \% & 5 s / 8 cores \\
GRES3D+SELAS \cite{Shinzato2015} & 87.57 \% & 90.52 \% & 85.92 \% & 89.28 \% & 16.08 \% & 10.72 \% & 110 ms / 4 core \\
CN \cite{Alvarez2012ECCV} & 86.21 \% & 84.40 \% & 82.85 \% & 89.86 \% & 20.45 \% & 10.14 \% & 2 s / 1 core \\
RES3D-Stereo \cite{Shinzato2014ITSC} & 83.62 \% & 85.74 \% & 79.81 \% & 87.81 \% & 24.42 \% & 12.19 \% & 0.7 s / 1 core \\
SPlane \cite{Einecke2014IV} & 82.28 \% & 82.83 \% & 76.85 \% & 88.53 \% & 29.32 \% & 11.47 \% & 2 s / 1 core \\
SPlane + BL \cite{Einecke2014IV} & 82.04 \% & 85.56 \% & 75.11 \% & 90.39 \% & 32.93 \% & 9.61 \% & 2 s / 1 core \\
ANN \cite{Vitor2013IV} & 80.95 \% & 68.36 \% & 69.95 \% & 96.05 \% & 45.35 \% & 3.95 \% & 3 s / 1 core
\end{tabular}