\begin{tabular}{c | c | c | c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf MaxF} & {\bf AP} & {\bf PRE} & {\bf REC} & {\bf FPR} & {\bf FNR} & {\bf Runtime} & {\bf Environment}\\ \hline
DFFA & & 94.31 \% & 88.03 \% & 95.33 \% & 93.31 \% & 0.80 \% & 6.69 \% & 0.4 s / GPU & \\
SAIT & & 93.25 \% & 87.94 \% & 95.24 \% & 91.34 \% & 0.80 \% & 8.66 \% & 0.04 s / GPU & \\
DCCN & la & 92.70 \% & 90.94 \% & 92.39 \% & 93.01 \% & 1.35 \% & 6.99 \% & 25 ms / GPU & \\
SAIT & & 92.29 \% & 88.39 \% & 93.31 \% & 91.28 \% & 1.15 \% & 8.72 \% & 0.04 s / GPU & \\
SSL & & 92.29 \% & 88.39 \% & 93.31 \% & 91.28 \% & 1.15 \% & 8.72 \% & 0.05 s / GPU & \\
NVLaneNet & & 91.86 \% & 91.42 \% & 90.89 \% & 92.85 \% & 1.64 \% & 7.15 \% & 0.08 s / GPU & \\
S-Lane & & 91.76 \% & 82.37 \% & 95.96 \% & 87.92 \% & 0.65 \% & 12.08 \% & 0.03 s / GPU & \\
RDBN & & 91.55 \% & 81.62 \% & 94.27 \% & 88.98 \% & 0.95 \% & 11.02 \% & 0.25 s / GPU & \\
FCN-GCBs & & 91.24 \% & 85.14 \% & 92.15 \% & 90.35 \% & 1.35 \% & 9.65 \% & 0.08 s / GPU & \\
RBNet & & 90.54 \% & 82.03 \% & 94.92 \% & 86.56 \% & 0.82 \% & 13.44 \% & 0.18 s / GPU & \\
JOJnet & & 90.49 \% & 83.84 \% & 89.97 \% & 91.01 \% & 1.79 \% & 8.99 \% & 0.1 s / GPU & \\
VPD & & 90.01 \% & 81.60 \% & 88.26 \% & 91.82 \% & 2.15 \% & 8.18 \% & 4 s / 1 core & \\
Up-Conv-Poly & & 89.88 \% & 87.52 \% & 92.01 \% & 87.84 \% & 1.34 \% & 12.16 \% & 0.08 s / GPU & G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.\\
SPRAY & & 83.42 \% & 86.84 \% & 84.76 \% & 82.12 \% & 2.60 \% & 17.88 \% & 45 ms / & T. Kuehnl, F. Kummert and J. Fritsch: Spatial Ray Features for Real-Time Ego-Lane Extraction. Proc. IEEE Intelligent Transportation Systems 2012.\\
FCNS & & 76.31 \% & 81.57 \% & 77.88 \% & 74.81 \% & 3.74 \% & 25.19 \% & 6 s / 1 core & \\
FCNB & & 75.85 \% & 79.27 \% & 71.49 \% & 80.77 \% & 5.67 \% & 19.23 \% & 0.2 s / 1 core & \\
PFH+HSV & & 75.85 \% & 79.27 \% & 71.49 \% & 80.77 \% & 5.67 \% & 19.23 \% & 0.1 s / 1 core & \\
RPP & & 75.85 \% & 79.27 \% & 71.49 \% & 80.77 \% & 5.67 \% & 19.23 \% & 0.16 s / & \\
FF & & 75.85 \% & 79.27 \% & 71.49 \% & 80.77 \% & 5.67 \% & 19.23 \% & 0.33 s / 1 core & \\
YhY & & 75.85 \% & 79.27 \% & 71.49 \% & 80.77 \% & 5.67 \% & 19.23 \% & 0.4 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
LKW & & 75.85 \% & 79.27 \% & 71.49 \% & 80.77 \% & 5.67 \% & 19.23 \% & 0.1 s / 1 core & \\
SPlane + BL & st & 69.63 \% & 73.78 \% & 80.01 \% & 61.63 \% & 2.71 \% & 38.37 \% & 2 s / 1 core & N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.\\
SCRFFPFHGSP & st & 57.22 \% & 39.34 \% & 41.78 \% & 90.79 \% & 22.28 \% & 9.21 \% & 5 s / 8 cores & I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.
\end{tabular}