\begin{tabular}{c | c | c | c | c | c | c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf PRE-20} & {\bf F1-20} & {\bf HR-20} & {\bf PRE-30} & {\bf F1-30} & {\bf HR-30} & {\bf PRE-40} & {\bf F1-40} & {\bf HR-40} & {\bf Runtime} & {\bf Environment}\\ \hline
DFFA & & 99.22 \% & 99.34 \% & 99.22 \% & 98.92 \% & 98.52 \% & 97.57 \% & 96.74 \% & 93.10 \% & 86.84 \% & 0.4 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
NVLaneNet & & 99.22 \% & 99.37 \% & 99.01 \% & 99.03 \% & 98.62 \% & 97.38 \% & 96.74 \% & 93.10 \% & 82.89 \% & 0.08 s / GPU & \\
DCCN & la & 99.10 \% & 99.08 \% & 98.87 \% & 98.79 \% & 98.19 \% & 97.38 \% & 96.70 \% & 90.70 \% & 89.33 \% & 25 ms / GPU & \\
AILabsLane & & 99.13 \% & 99.06 \% & 98.79 \% & 99.22 \% & 97.91 \% & 96.60 \% & 96.74 \% & 87.36 \% & 86.84 \% & 0.25 s / GPU & \\
FCN-GCBs & & 99.18 \% & 99.13 \% & 98.76 \% & 99.00 \% & 98.11 \% & 96.43 \% & 95.56 \% & 93.02 \% & 81.58 \% & 0.08 s / GPU & \\
RBNet & & 99.24 \% & 99.33 \% & 99.21 \% & 98.74 \% & 97.34 \% & 95.92 \% & 95.56 \% & 87.21 \% & 81.58 \% & 0.18 s / GPU & Z. Chen and Z. Chen: RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection. International Conference on Neural Information Processing 2017.\\
VD & & 99.12 \% & 98.87 \% & 97.71 \% & 99.07 \% & 97.93 \% & 95.58 \% & 96.70 \% & 90.70 \% & 78.67 \% & 4 s / 1 core & \\
Up-Conv-Poly & & 99.06 \% & 98.84 \% & 98.45 \% & 97.57 \% & 95.27 \% & 93.14 \% & 90.11 \% & 83.72 \% & 77.63 \% & 0.08 s / GPU & G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.\\
SPRAY & & 97.58 \% & 96.74 \% & 96.38 \% & 96.59 \% & 94.16 \% & 92.06 \% & 87.64 \% & 78.57 \% & 62.16 \% & 45 ms / & T. Kuehnl, F. Kummert and J. Fritsch: Spatial Ray Features for Real-Time Ego-Lane Extraction. Proc. IEEE Intelligent Transportation Systems 2012.\\
YhY & & 95.83 \% & 92.92 \% & 89.16 \% & 94.41 \% & 89.44 \% & 83.62 \% & 84.04 \% & 66.67 \% & 50.00 \% & 0.4 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
LKW & & 95.83 \% & 92.92 \% & 89.16 \% & 94.41 \% & 89.44 \% & 83.62 \% & 84.04 \% & 66.67 \% & 50.00 \% & 0.1 s / 1 core & \\
SPlane + BL & st & 95.53 \% & 92.88 \% & 91.21 \% & 91.89 \% & 87.12 \% & 74.28 \% & 79.79 \% & 47.13 \% & 0.00 \% & 2 s / 1 core & N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.\\
Strait & & 94.97 \% & 91.74 \% & 88.38 \% & 89.60 \% & 81.85 \% & 73.89 \% & 67.02 \% & 48.28 \% & 32.89 \% & 69 ms / GPU & \\
SCRFFPFHGSP & st & 94.88 \% & 87.95 \% & 82.98 \% & 87.91 \% & 78.90 \% & 71.95 \% & 60.64 \% & 43.68 \% & 38.16 \% & 5 s / 8 cores & I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.
\end{tabular}