\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
CyberMELD & la gp & 99.17 \% & 99.23 \% & 99.11 \% & 98.64 \% & 98.00 \% & 97.55 \% & 94.57 \% & 89.66 \% & 90.79 \% & 0.05 s / 8 core & X. Wang, Y. Qian, C. Wang and M. Yang: Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios. IEEE Access 2020.\\
CyberMELD+PLARD & la gp & 99.18 \% & 99.36 \% & 99.29 \% & 98.70 \% & 98.20 \% & 97.17 \% & 96.74 \% & 90.80 \% & 90.79 \% & 0.18 s / 8 cores & X. Wang, Y. Qian, C. Wang and M. Yang: Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios. IEEE Access 2020.\\
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.\\
RoadNet3 & & 99.18 \% & 99.21 \% & 99.07 \% & 98.39 \% & 97.23 \% & 95.57 \% & 94.57 \% & 83.72 \% & 80.26 \% & 300 ms / GPU & Y. Lyu, L. Bai and X. Huang: Road Segmentation using CNN and Distributed LSTM. 2019 IEEE International Symposium on Circuits and Systems (ISCAS) 2019.\\
ECPrior (No ML) & & 98.70 \% & 97.95 \% & 96.22 \% & 98.65 \% & 97.28 \% & 93.96 \% & 96.70 \% & 91.86 \% & 80.00 \% & 1 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.\\
SAFE & st & 98.68 \% & 97.88 \% & 95.73 \% & 98.12 \% & 94.95 \% & 89.77 \% & 90.48 \% & 76.25 \% & 55.07 \% & 0.06 s / 2 cores & \\
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.\\
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}