\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
NVLaneNet & & 91.86 \% & 91.42 \% & 90.89 \% & 92.85 \% & 1.64 \% & 7.15 \% & 0.08 s / GPU & \\
ILN & & 91.62 \% & 91.17 \% & 91.98 \% & 91.26 \% & 1.40 \% & 8.74 \% & 0.24 s / 1 core & \\
RBNet & & 90.54 \% & 82.03 \% & 94.92 \% & 86.56 \% & 0.82 \% & 13.44 \% & 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.\\
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.\\
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.\\
test-001 & & 67.77 \% & 55.50 \% & 55.94 \% & 85.96 \% & 11.92 \% & 14.04 \% & 0.2 s / 1 core & \\
DH-OCR & & 63.60 \% & 44.14 \% & 47.05 \% & 98.08 \% & 19.43 \% & 1.92 \% & 0.2 s / GPU & \\
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}