\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
SNE-RoadSegV2 \cite{Feng2024sne} & 97.08 \% & 92.87 \% & 96.83 \% & 97.34 \% & 1.04 \% & 2.66 \% & 0.03 s / GPU \\
SNE-RoadSeg+ \cite{wang2021sne} & 97.04 \% & 92.97 \% & 96.84 \% & 97.24 \% & 1.03 \% & 2.76 \% & 0.08 s / GPU \\
RoadFormer \cite{li2023roadformer} & 97.02 \% & 92.78 \% & 96.61 \% & 97.43 \% & 1.12 \% & 2.57 \% & 0.07 s / GPU \\
PLB-RD \cite{sun2022pseudo} & 96.93 \% & 93.08 \% & 96.78 \% & 97.09 \% & 1.05 \% & 2.91 \% & 0.46 s / GPU \\
DFM-RTFNet \cite{wang2021dynamic} & 96.26 \% & 93.01 \% & 96.16 \% & 96.35 \% & 1.25 \% & 3.65 \% & 0.08 s / GPU \\
LRDNet+ \cite{lrdnet2022} & 96.18 \% & 90.03 \% & 95.94 \% & 96.42 \% & 1.33 \% & 3.58 \% & 0.01 s / GPU \\
USNet \cite{Chang22Fast} & 96.11 \% & 91.71 \% & 95.86 \% & 96.37 \% & 1.36 \% & 3.63 \% & 0.02 s / GPU \\
LRDNet (L) \cite{lrdnet2022} & 96.10 \% & 89.59 \% & 95.97 \% & 96.22 \% & 1.32 \% & 3.78 \% & 0.1 s / GPU \\
SNE-RoadSeg \cite{fan2020sneroadseg} & 96.03 \% & 93.03 \% & 96.22 \% & 95.83 \% & 1.23 \% & 4.17 \% & 0.18 s / GPU \\
PLARD \cite{chen2019progressive} & 95.95 \% & 95.25 \% & 96.25 \% & 95.65 \% & 1.21 \% & 4.35 \% & 0.16 s / GPU \\
LRDNet(S) \cite{lrdnet2022} & 95.78 \% & 90.75 \% & 95.62 \% & 95.95 \% & 1.43 \% & 4.05 \% & .009 s / GPU \\
CLCFNet \cite{GuYK21} & 95.68 \% & 88.37 \% & 94.75 \% & 96.63 \% & 1.75 \% & 3.37 \% & 0.02 s / GPU \\
CLCFNet (LiDAR) \cite{GuYK21} & 95.25 \% & 88.02 \% & 94.36 \% & 96.16 \% & 1.87 \% & 3.84 \% & 0.02 s / GPU \\
NIM-RTFNet \cite{wang2020applying} & 95.11 \% & 92.94 \% & 95.91 \% & 94.32 \% & 1.31 \% & 5.68 \% & 0.05 s / GPU \\
RBANet \cite{sun2019reverse} & 94.91 \% & 86.35 \% & 92.53 \% & 97.42 \% & 2.56 \% & 2.58 \% & 0.16 s / GPU \\
LidCamNet \cite{1809.07941} & 94.54 \% & 92.74 \% & 94.64 \% & 94.45 \% & 1.74 \% & 5.55 \% & 0.15 s / GPU \\
RGB36-Cotrain \cite{CaltagironeEtAl2019} & 94.53 \% & 92.54 \% & 94.60 \% & 94.46 \% & 1.76 \% & 5.54 \% & 0.1 s / 1 core \\
SSLGAN \cite{Han2018Semisupervised} & 94.40 \% & 87.84 \% & 94.17 \% & 94.63 \% & 1.91 \% & 5.37 \% & 700ms / GPU \\
LC-CRF \cite{GuZTYK19} & 94.01 \% & 85.24 \% & 91.31 \% & 96.88 \% & 3.00 \% & 3.12 \% & 0.18 s / GPU \\
MultiNet \cite{DBLPjournalscorrTeichmannWZCU16} & 93.69 \% & 92.55 \% & 94.24 \% & 93.14 \% & 1.85 \% & 6.86 \% & 0.17 s / GPU \\
TVFNet \cite{GuZYAK19} & 93.65 \% & 87.57 \% & 93.87 \% & 93.43 \% & 1.99 \% & 6.57 \% & 0.04 s / GPU \\
StixelNet II \cite{DanLevi2017ICCV} & 93.40 \% & 85.01 \% & 91.05 \% & 95.87 \% & 3.07 \% & 4.13 \% & 1.2 s / 1 core \\
HA-DeepLabv3+ \cite{fan2020tmech} & 93.24 \% & 91.83 \% & 93.19 \% & 93.28 \% & 2.22 \% & 6.72 \% & 0.06 s / 1 core \\
RBNet \cite{chen2017rbnet} & 93.21 \% & 89.18 \% & 92.81 \% & 93.60 \% & 2.36 \% & 6.40 \% & 0.18 s / GPU \\
Hadamard-FCN \cite{Oeljeklaus21} & 93.14 \% & 90.00 \% & 93.31 \% & 92.98 \% & 2.17 \% & 7.02 \% & 0.02 s / GPU \\
BJN \cite{s21227623} & 93.13 \% & 87.59 \% & 93.89 \% & 92.38 \% & 1.96 \% & 7.62 \% & 0.02 s / 1 core \\
RoadNet3 \cite{lyu2019road} & 92.95 \% & 91.93 \% & 93.32 \% & 92.58 \% & 2.16 \% & 7.42 \% & 300 ms / GPU \\
ChipNet \cite{8580596} & 92.91 \% & 84.95 \% & 90.98 \% & 94.91 \% & 3.06 \% & 5.09 \% & 12 ms / GPU \\
TEDNet \cite{10.1007978303115471336} & 92.78 \% & 91.08 \% & 91.86 \% & 93.71 \% & 2.71 \% & 6.29 \% & 0.09 s / GPU \\
Up-Conv-Poly \cite{Oliveira2016IROS} & 92.65 \% & 89.20 \% & 92.85 \% & 92.45 \% & 2.32 \% & 7.55 \% & 0.08 s / GPU \\
OFA Net \cite{zhang2019one} & 92.62 \% & 83.12 \% & 88.97 \% & 96.58 \% & 3.90 \% & 3.42 \% & 0.04 s / GPU \\
CLRD \cite{10.1007978303115471336} & 92.41 \% & 90.16 \% & 92.30 \% & 92.52 \% & 2.52 \% & 7.48 \% & 0.05 s / GPU \\
LoDNN \cite{CaltagironeEtAl2016} & 92.29 \% & 90.35 \% & 90.81 \% & 93.81 \% & 3.09 \% & 6.19 \% & 18 ms / GPU \\
Up-Conv \cite{Oliveira2016IROS} & 91.89 \% & 89.44 \% & 92.59 \% & 91.20 \% & 2.38 \% & 8.80 \% & 0.05 s / GPU \\
DDN \cite{Mohan2014ARXIV} & 91.76 \% & 86.84 \% & 93.06 \% & 90.50 \% & 2.20 \% & 9.50 \% & 2 s / GPU \\
DEEP-DIG \cite{munozbulnesdeep2017} & 91.27 \% & 91.77 \% & 91.32 \% & 91.22 \% & 2.82 \% & 8.78 \% & 0.14 s / GPU \\
RGB36-Super \cite{CaltagironeEtAl2019} & 91.15 \% & 90.16 \% & 89.68 \% & 92.68 \% & 3.48 \% & 7.32 \% & 0.1 s / 1 core \\
RoadNet-RT \cite{bai2020roadnet} & 90.79 \% & 91.67 \% & 91.79 \% & 89.80 \% & 2.62 \% & 10.20 \% & 8m s / GPU \\
HID-LS \cite{GuZYK17} & 89.81 \% & 82.33 \% & 88.11 \% & 91.58 \% & 4.03 \% & 8.42 \% & 0.25 s / 1 cores \\
FTP \cite{Laddha2016IV} & 89.62 \% & 88.93 \% & 89.10 \% & 90.14 \% & 3.59 \% & 9.86 \% & 0.28 s / GPU \\
ALO-AVG-MM \cite{Reis2019IJCNN2019} & 89.45 \% & 79.87 \% & 85.40 \% & 93.90 \% & 5.23 \% & 6.10 \% & 0.0296 sec / \\
HybridCRF \cite{XIAO2018HybridCRF} & 88.53 \% & 80.79 \% & 86.41 \% & 90.76 \% & 4.65 \% & 9.24 \% & 1.5 s / 1 core \\
LidarHisto \cite{7989159} & 86.55 \% & 81.13 \% & 90.71 \% & 82.75 \% & 2.76 \% & 17.25 \% & 0.1 s / 1 core \\
FCN-LC \cite{Mendes2016ICRA} & 86.27 \% & 75.37 \% & 86.65 \% & 85.89 \% & 4.31 \% & 14.11 \% & 0.03 s / \\
CB \cite{Mendes2015ARXIV} & 86.13 \% & 75.21 \% & 86.47 \% & 85.80 \% & 4.38 \% & 14.20 \% & 2 s / 1 core \\
StixelNet \cite{Levi2015BMVC} & 86.06 \% & 72.05 \% & 82.61 \% & 89.82 \% & 6.16 \% & 10.18 \% & 1 s / GPU \\
HIM \cite{Munoz2010ECCV} & 85.76 \% & 76.18 \% & 87.65 \% & 83.95 \% & 3.86 \% & 16.05 \% & 7 s / >8 cores \\
MixedCRF \cite{Han2017Road} & 85.69 \% & 75.12 \% & 80.17 \% & 92.02 \% & 7.42 \% & 7.98 \% & 6s / 1 core \\
NNP \cite{Chen2015NIPS} & 85.55 \% & 76.90 \% & 85.36 \% & 85.75 \% & 4.79 \% & 14.25 \% & 5 s / 4 cores \\
BMCF \cite{wang2016multi} & 85.46 \% & 74.07 \% & 85.06 \% & 85.86 \% & 4.91 \% & 14.14 \% & 2.5 s / 1 core \\
FusedCRF \cite{Xiao2015IV} & 84.49 \% & 72.35 \% & 77.13 \% & 93.40 \% & 9.02 \% & 6.60 \% & 2 s / 1 core \\
MAP \cite{Laddha2016IV} & 84.44 \% & 87.17 \% & 83.66 \% & 85.23 \% & 5.42 \% & 14.77 \% & 0.28s / \\
GRES3D+VELO \cite{Shinzato2015} & 84.14 \% & 80.20 \% & 80.57 \% & 88.03 \% & 6.92 \% & 11.97 \% & 60 ms / 4 cores \\
RES3D-Velo \cite{Shinzato2014IV} & 83.63 \% & 72.58 \% & 77.38 \% & 90.97 \% & 8.67 \% & 9.03 \% & 0.36 s / 1 core \\
SPRAY \cite{Kuehnl2012ITSC} & 82.71 \% & 87.19 \% & 82.16 \% & 83.26 \% & 5.89 \% & 16.74 \% & 45 ms / \\
GRES3D+SELAS \cite{Shinzato2015} & 82.70 \% & 83.95 \% & 78.54 \% & 87.32 \% & 7.77 \% & 12.68 \% & 110 ms / 4 core \\
geo+gpr+crf \cite{doi10.11771729881417717058} & 81.00 \% & 69.74 \% & 79.78 \% & 82.27 \% & 6.79 \% & 17.73 \% & 30 s / 1 core \\
SCRFFPFHGSP \cite{Gheorghe2015} & 80.78 \% & 70.80 \% & 81.07 \% & 80.50 \% & 6.13 \% & 19.50 \% & 5 s / 8 cores \\
ProbBoost \cite{Vitor2014ICRAWORK} & 80.76 \% & 68.70 \% & 85.25 \% & 76.72 \% & 4.33 \% & 23.28 \% & 2.5 min / >8 cores \\
multi-task CNN \cite{Oeljeklaus18} & 80.45 \% & 75.87 \% & 68.63 \% & 97.19 \% & 14.48 \% & 2.81 \% & 25.1 ms / GPU \\
PGM-ARS \cite{Passani15IV} & 79.94 \% & 67.77 \% & 77.37 \% & 82.67 \% & 7.88 \% & 17.33 \% & 0.05 s / i74700MQ \\
RES3D-Stereo \cite{Shinzato2014ITSC} & 78.75 \% & 73.60 \% & 77.63 \% & 79.90 \% & 7.50 \% & 20.10 \% & 0.7 s / 1 core \\
BM \cite{Wang2014IVWORK} & 78.43 \% & 62.46 \% & 70.87 \% & 87.80 \% & 11.76 \% & 12.20 \% & 2 s / 2 cores \\
SRF \cite{Xiao2016IJARS} & 76.07 \% & 79.97 \% & 71.47 \% & 81.31 \% & 10.57 \% & 18.69 \% & 0.2 s / 1 core \\
HistonBoost \cite{GioIV14} & 74.19 \% & 63.01 \% & 77.43 \% & 71.22 \% & 6.77 \% & 28.78 \% & 2.5 min / >8 cores \\
SPlane + BL \cite{Einecke2014IV} & 74.02 \% & 79.61 \% & 65.15 \% & 85.68 \% & 14.93 \% & 14.32 \% & 2 s / 1 core \\
SPlane \cite{Einecke2014IV} & 73.30 \% & 69.11 \% & 65.39 \% & 83.38 \% & 14.38 \% & 16.62 \% & 2 s / 1 core \\
CN \cite{Alvarez2012ECCV} & 72.25 \% & 66.61 \% & 71.96 \% & 72.54 \% & 9.21 \% & 27.46 \% & 2 s / 1 core \\
ARSL-AMI \cite{Passani2014ITSC} & 70.33 \% & 61.97 \% & 83.33 \% & 60.84 \% & 3.97 \% & 39.16 \% & 0.05 s / 4 cores \\
ANN \cite{Vitor2013IV} & 54.07 \% & 36.61 \% & 39.28 \% & 86.69 \% & 43.67 \% & 13.31 \% & 3 s / 1 core
\end{tabular}