\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
TuSimple & & 97.62 \% & 95.53 \% & 97.41 \% & 97.82 \% & 2.86 \% & 2.18 \% & 0.2s / GPU & \\
YhY & & 97.42 \% & 93.08 \% & 97.15 \% & 97.68 \% & 3.15 \% & 2.32 \% & 0.4 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
KRSF & & 97.34 \% & 95.58 \% & 97.42 \% & 97.26 \% & 2.83 \% & 2.74 \% & 0.3 s / GPU & \\
UNV & & 97.34 \% & 94.23 \% & 97.52 \% & 97.16 \% & 2.71 \% & 2.84 \% & 1.2 s / GPU & \\
SAIT & & 97.31 \% & 92.68 \% & 96.71 \% & 97.91 \% & 3.66 \% & 2.09 \% & 0.04 s / GPU & \\
KRS & & 97.27 \% & 95.55 \% & 97.19 \% & 97.34 \% & 3.09 \% & 2.66 \% & 0.3 s / GPU & \\
DFFA & & 97.26 \% & 92.75 \% & 96.79 \% & 97.74 \% & 3.56 \% & 2.26 \% & 0.4 s / GPU & \\
SAIT & & 97.15 \% & 93.34 \% & 97.44 \% & 96.86 \% & 2.79 \% & 3.14 \% & 0.04 s / GPU & \\
DCCN & la & 97.10 \% & 95.31 \% & 96.64 \% & 97.56 \% & 3.73 \% & 2.44 \% & 25 ms / GPU & \\
RPP & & 97.03 \% & 92.36 \% & 96.36 \% & 97.70 \% & 4.06 \% & 2.30 \% & 0.16 s / & \\
MVnet & la & 96.99 \% & 93.94 \% & 98.10 \% & 95.90 \% & 2.04 \% & 4.10 \% & 0.04 s / 1 core & \\
WSLGAN & & 96.95 \% & 92.87 \% & 96.92 \% & 96.98 \% & 3.39 \% & 3.02 \% & 800ms / GPU & \\
baseline & & 96.88 \% & 95.39 \% & 96.36 \% & 97.40 \% & 4.04 \% & 2.60 \% & 0.05 s / 1 core & \\
RSNet & & 96.85 \% & 95.26 \% & 96.79 \% & 96.91 \% & 3.54 \% & 3.09 \% & 0.09 s / GPU & \\
KRS & & 96.84 \% & 95.49 \% & 96.70 \% & 96.98 \% & 3.64 \% & 3.02 \% & 1 s / GPU & \\
SSLGAN & & 96.72 \% & 92.99 \% & 97.05 \% & 96.40 \% & 3.22 \% & 3.60 \% & 700ms / GPU & \\
RSNet2 & & 96.60 \% & 95.28 \% & 96.57 \% & 96.63 \% & 3.78 \% & 3.37 \% & 0.07 s / GPU & \\
CoDNN & & 96.56 \% & 95.33 \% & 96.41 \% & 96.71 \% & 3.96 \% & 3.29 \% & 0.01 s / 1 core & \\
RSNet- & & 96.40 \% & 95.34 \% & 96.59 \% & 96.22 \% & 3.74 \% & 3.78 \% & 0.07 s / GPU & \\
StixelNet II & & 96.22 \% & 91.24 \% & 95.13 \% & 97.33 \% & 5.48 \% & 2.67 \% & 1.2 s / 1 core & N. Garnett, S. Silberstein, S. Oron, E. Fetaya, U. Verner, A. Ayash, V. Goldner, R. Cohen, K. Horn and D. Levi: Real-time category-based and general obstacle detection for autonomous driving. 5th Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving (CVRSUAD'17, IEEE-ICCV 2017 Workshop) 2017.\\
AXN & & 96.20 \% & 95.29 \% & 95.91 \% & 96.49 \% & 4.52 \% & 3.51 \% & 0.06 s / GPU & \\
MultiNet & & 96.15 \% & 95.36 \% & 95.79 \% & 96.51 \% & 4.67 \% & 3.49 \% & 0.17 s / GPU & M. Teichmann, M. Weber, J. Zoellner, R. Cipolla and R. Urtasun: MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving. CoRR 2016.\\
MMN & & 96.15 \% & 94.98 \% & 96.07 \% & 96.22 \% & 4.33 \% & 3.78 \% & 0.1 s / GPU & \\
MBN & & 96.14 \% & 91.43 \% & 95.33 \% & 96.96 \% & 5.22 \% & 3.04 \% & 0.16 s / GPU & \\
wt & & 96.11 \% & 95.44 \% & 96.08 \% & 96.14 \% & 4.31 \% & 3.86 \% & 0.1 s / GPU & \\
TDCac1 CNN & & 96.11 \% & 92.62 \% & 96.65 \% & 95.57 \% & 3.64 \% & 4.43 \% & .093 s / 1 core & \\
FNETMS & & 96.08 \% & 94.95 \% & 95.56 \% & 96.60 \% & 4.93 \% & 3.40 \% & 0.04 s / GPU & \\
RBNet & & 96.06 \% & 93.49 \% & 95.80 \% & 96.31 \% & 4.64 \% & 3.69 \% & 0.18 s / GPU & \\
LoDNN & la & 96.05 \% & 95.03 \% & 95.79 \% & 96.31 \% & 4.66 \% & 3.69 \% & 18 ms / GPU & L. Caltagirone, S. Scheidegger, L. Svensson and M. Wahde: Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks. IEEE Intelligent Vehicles Symposium 2017.\\
SSL & & 96.01 \% & 93.25 \% & 96.43 \% & 95.59 \% & 3.89 \% & 4.41 \% & 0.05 s / GPU & \\
FusionNet & st & 96.01 \% & 94.38 \% & 95.22 \% & 96.81 \% & 5.34 \% & 3.19 \% & 0.3 s / GPU & \\
FF & & 95.79 \% & 93.85 \% & 98.00 \% & 93.68 \% & 2.10 \% & 6.32 \% & 0.33 s / 1 core & \\
FuseNet & st & 95.61 \% & 95.80 \% & 95.29 \% & 95.93 \% & 5.21 \% & 4.07 \% & 0.2 s / GPU & \\
Up-Conv-Poly & & 95.52 \% & 92.86 \% & 95.37 \% & 95.67 \% & 5.10 \% & 4.33 \% & 0.08 s / GPU & G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.\\
DEEP-DIG & & 95.45 \% & 95.41 \% & 95.49 \% & 95.41 \% & 4.96 \% & 4.59 \% & 0.14 s / GPU & J. Muñoz-Bulnes, C. Fernandez, I. Parra, D. Fernández-Llorca and M. Sotelo: Deep Fully Convolutional Networks with Random Data Augmentation for Enhanced Generalization in Road Detection. Workshop on Deep Learning for Autonomous Driving on IEEE 20th International Conference on Intelligent Transportation Systems 2017.\\
ResNetPK & la & 95.45 \% & 92.27 \% & 96.26 \% & 94.65 \% & 4.04 \% & 5.35 \% & 0.4s / GPU & \\
RDSN & & 95.32 \% & 91.01 \% & 94.87 \% & 95.76 \% & 5.69 \% & 4.24 \% & 0.25 s / GPU & \\
FCN-GCBs & & 95.29 \% & 91.27 \% & 95.16 \% & 95.43 \% & 5.33 \% & 4.57 \% & 0.08 s / GPU & \\
RDBN & & 95.24 \% & 93.78 \% & 96.01 \% & 94.48 \% & 4.31 \% & 5.52 \% & 0.25 s / GPU & \\
s-FCN-loc & & 95.01 \% & 91.86 \% & 95.81 \% & 94.23 \% & 4.53 \% & 5.77 \% & 0.4 s / GPU & J. Gao, Q. Wang and Y. Yuan: Embedding structured contour and location prior in siamesed fully convolutional networks for road detection. Robotics and Automation (ICRA), 2017 IEEE International Conference on 2017.\\
RSNetVGG & & 94.88 \% & 95.00 \% & 95.73 \% & 94.05 \% & 4.61 \% & 5.95 \% & 0.09 s / GPU & \\
DFN & & 94.63 \% & 92.15 \% & 94.59 \% & 94.67 \% & 5.95 \% & 5.33 \% & 0.25 s / GPU & \\
HID-LS & la & 94.36 \% & 91.01 \% & 94.88 \% & 93.84 \% & 5.57 \% & 6.16 \% & 0.5 s / 4 cores & \\
VGGFCN & & 94.26 \% & 91.14 \% & 95.02 \% & 93.51 \% & 5.38 \% & 6.49 \% & 0.4 s / GPU & \\
DDN & & 94.17 \% & 92.70 \% & 96.73 \% & 91.74 \% & 3.41 \% & 8.26 \% & 2 s / GPU & R. Mohan: Deep Deconvolutional Networks for Scene Parsing. 2014.\\
FCN-LC & & 94.09 \% & 90.26 \% & 94.05 \% & 94.13 \% & 6.55 \% & 5.87 \% & 0.03 s / & C. Mendes, V. Frémont and D. Wolf: Exploiting Fully Convolutional Neural Networks for Fast Road Detection. IEEE Conference on Robotics and Automation (ICRA) 2016.\\
Up-Conv & & 93.89 \% & 92.62 \% & 94.57 \% & 93.22 \% & 5.89 \% & 6.78 \% & 0.05 s / GPU & G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.\\
HIM & & 93.55 \% & 90.38 \% & 94.18 \% & 92.92 \% & 6.31 \% & 7.08 \% & 7 s / >8 cores & D. Munoz, J. Bagnell and M. Hebert: Stacked Hierarchical Labeling. European Conference on Computer Vision (ECCV) 2010.\\
Feature++ & st & 93.55 \% & 92.34 \% & 92.77 \% & 94.34 \% & 8.08 \% & 5.66 \% & 13 s / 4 core & \\
LiDAR-SPHnet & la & 93.54 \% & 89.86 \% & 93.45 \% & 93.63 \% & 7.22 \% & 6.37 \% & 0.14 s / GPU & \\
JOJnet & & 93.42 \% & 88.88 \% & 91.93 \% & 94.96 \% & 9.16 \% & 5.04 \% & 0.1 s / GPU & \\
LidarHisto & la & 93.32 \% & 93.19 \% & 95.39 \% & 91.34 \% & 4.85 \% & 8.66 \% & 0.1 s / 1 core & L. Chen, J. Yang and H. Kong: Lidar-histogram for fast road and obstacle detection. 2017 IEEE International Conference on Robotics and Automation (ICRA) 2017.\\
StixelNet & & 93.26 \% & 87.15 \% & 90.63 \% & 96.06 \% & 10.92 \% & 3.94 \% & 1 s / GPU & D. Levi, N. Garnett and E. Fetaya: StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation.. 26TH British Machine Vision Conference (BMVC) 2015.\\
HFM & & 93.12 \% & 87.10 \% & 90.58 \% & 95.82 \% & 10.96 \% & 4.18 \% & 5 s / 2 cores & \\
ResAXN & & 92.99 \% & 94.76 \% & 93.75 \% & 92.24 \% & 6.76 \% & 7.76 \% & 0.06 s / GPU & \\
FTP & & 92.98 \% & 92.89 \% & 91.84 \% & 94.15 \% & 9.20 \% & 5.85 \% & 0.28 s / GPU & A. Laddha, M. Kocamaz, L. Navarro-Serment and M. Hebert: Map-Supervised Road Detection. IEEE Intelligent Vehicles Symposium Proceedings 2016.\\
LWDS & & 92.81 \% & 94.66 \% & 94.41 \% & 91.25 \% & 5.93 \% & 8.75 \% & 0.07 s / GPU & \\
MixedCRF & la & 92.75 \% & 90.24 \% & 94.03 \% & 91.50 \% & 6.39 \% & 8.50 \% & 6s / 1 core & \\
SGL & & 92.39 \% & 87.73 \% & 95.59 \% & 89.40 \% & 4.53 \% & 10.60 \% & 0.01 s / 1 core & \\
BNV & st & 92.21 \% & 87.99 \% & 91.55 \% & 92.89 \% & 9.43 \% & 7.11 \% & 3 s / 2 cores & \\
HybridCRF & la & 91.95 \% & 86.44 \% & 94.01 \% & 89.98 \% & 6.30 \% & 10.02 \% & 1.5 s / 1 core & L. Xiao, R. Wang, B. Dai, Y. Fang, D. Liu and T. Wu: Hybrid Conditional Random Field based Camera-LIDAR Fusion for Road Detection. Information Sciences. in press.\\
PGM-ARS & & 91.76 \% & 84.80 \% & 88.05 \% & 95.80 \% & 14.30 \% & 4.20 \% & 0.05 s / i74700MQ & M. Passani, J. Yebes and L. Bergasa: Fast Pixelwise Road Inference Based on Uniformly Reweighted Belief Propagation . Proc. IEEE Intelligent Vehicles Symposium 2015.\\
TFSeg & & 91.41 \% & 93.00 \% & 91.68 \% & 91.15 \% & 9.09 \% & 8.85 \% & 0.07 s / GPU & \\
LiDAR-SPHnet & la & 91.40 \% & 91.89 \% & 89.03 \% & 93.90 \% & 12.72 \% & 6.10 \% & 52 ms / FPGA & \\
ProbBoost & st & 91.36 \% & 84.92 \% & 88.18 \% & 94.78 \% & 13.97 \% & 5.22 \% & 2.5 min / >8 cores & G. Vitor, A. Victorino and J. Ferreira: A probabilistic distribution approach for the classification of urban roads in complex environments. Workshop on Modelling, Estimation, Perception and Control of All Terrain Mobile Robots on IEEE International Conference on Robotics and Automation (ICRA) 2014.\\
NNP & st & 91.34 \% & 88.65 \% & 91.07 \% & 91.60 \% & 9.87 \% & 8.40 \% & 5 s / 4 cores & X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.\\
Multimodal & & 91.31 \% & 94.23 \% & 89.75 \% & 92.92 \% & 11.67 \% & 7.08 \% & 0.3 s / 4 cores & \\
FCNS & & 91.28 \% & 93.68 \% & 90.51 \% & 92.07 \% & 10.61 \% & 7.93 \% & 6 s / 1 core & \\
FRS\_SP & & 90.96 \% & 84.63 \% & 87.86 \% & 94.29 \% & 14.32 \% & 5.71 \% & 0.21 s / 4 cores & \\
SRF & & 90.77 \% & 92.44 \% & 89.35 \% & 92.23 \% & 12.08 \% & 7.77 \% & 0.2 s / 1 core & L. Xiao, B. Dai, D. Liu, D. Zhao and T. Wu: Monocular Road Detection Using Structured Random Forest. Int J Adv Robot Syst 2016.\\
RES3D-Velo & la & 90.60 \% & 85.38 \% & 85.96 \% & 95.78 \% & 17.20 \% & 4.22 \% & 0.36 s / 1 core & P. Shinzato, D. Wolf and C. Stiller: Road Terrain Detection: Avoiding Common Obstacle Detection Assumptions Using Sensor Fusion. Intelligent Vehicles Symposium (IV) 2014.\\
CB & & 90.55 \% & 85.40 \% & 92.75 \% & 88.45 \% & 7.60 \% & 11.55 \% & 2 s / 1 core & C. Mendes, V. Frémont and D. Wolf: Vision-Based Road Detection using Contextual Blocks. 2015.\\
FCNB & & 90.21 \% & 86.28 \% & 89.67 \% & 90.77 \% & 11.50 \% & 9.23 \% & 0.2 s / 1 core & \\
MAP & & 89.97 \% & 92.14 \% & 87.47 \% & 92.62 \% & 14.58 \% & 7.38 \% & 0.28s / & A. Laddha, M. Kocamaz, L. Navarro-Serment and M. Hebert: Map-Supervised Road Detection. IEEE Intelligent Vehicles Symposium Proceedings 2016.\\
DVFCN & & 89.95 \% & 93.93 \% & 89.74 \% & 90.17 \% & 11.33 \% & 9.83 \% & 0.07 s / GPU & \\
SPRAY & & 89.69 \% & 93.84 \% & 89.13 \% & 90.25 \% & 12.10 \% & 9.75 \% & 45 ms / & T. Kuehnl, F. Kummert and J. Fritsch: Spatial Ray Features for Real-Time Ego-Lane Extraction. Proc. IEEE Intelligent Transportation Systems 2012.\\
ARSL-AMI & & 89.56 \% & 82.82 \% & 85.87 \% & 93.59 \% & 16.93 \% & 6.41 \% & 0.05 s / 4 cores & M. Passani, J. Yebes and L. Bergasa: CRF-based semantic labeling in miniaturized road scenes . Proc. IEEE Intelligent Transportation Systems 2014.\\
FusedCRF & la & 89.51 \% & 83.53 \% & 86.64 \% & 92.58 \% & 15.69 \% & 7.42 \% & 2 s / 1 core & L. Xiao, B. Dai, D. Liu, T. Hu and T. Wu: CRF based Road Detection with Multi-Sensor Fusion. Intelligent Vehicles Symposium (IV) 2015.\\
BM & st & 89.41 \% & 80.61 \% & 83.43 \% & 96.30 \% & 21.02 \% & 3.70 \% & 2 s / 2 cores & B. WANG, V. Fremont and S. Rodriguez Florez: Color-based Road Detection and its Evaluation on the KITTI Road Benchmark. Workshop on Benchmarking Road Terrain and Lane Detection Algorithms for In-Vehicle Application, IEEE Intelligent Vehicles Symposium 2014.\\
RFH & & 89.34 \% & 79.11 \% & 81.78 \% & 98.42 \% & 24.10 \% & 1.58 \% & .5 s / 1 core & \\
HistonBoost & st & 88.73 \% & 81.57 \% & 84.49 \% & 93.42 \% & 18.85 \% & 6.58 \% & 2.5 min / >8 cores & G. Vitor, A. Victorino and J. Ferreira: Comprehensive Performance Analysis of Road Detection Algorithms Using the Common Urban Kitti-Road Benchmark. Workshop on Benchmarking Road Terrain and Lane Detection Algorithms for In-Vehicle Application on IEEE Intelligent Vehicles Symposium (IV) 2014.\\
SegNet & & 88.59 \% & 83.54 \% & 88.35 \% & 88.84 \% & 12.88 \% & 11.16 \% & 0.01 s / 8 cores & \\
PFH+HSV & & 88.39 \% & 83.32 \% & 90.20 \% & 86.65 \% & 10.35 \% & 13.35 \% & 0.1 s / 1 core & \\
geo+gpr+crf & st & 88.20 \% & 82.33 \% & 85.32 \% & 91.27 \% & 17.26 \% & 8.73 \% & 30 s / 1 core & Z. Xiao, B. Dai, H. Li, T. Wu, X. Xu, Y. Zeng and T. Chen: Gaussian process regression-based robust free space detection for autonomous vehicle by 3-D point cloud and 2-D appearance information fusion. International Journal of Advanced Robotic Systems 2017.\\
GRES3D+VELO & la & 88.19 \% & 88.65 \% & 83.98 \% & 92.85 \% & 19.48 \% & 7.15 \% & 60 ms / 4 cores & P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.\\
SCRFFPFHGSP & st & 87.96 \% & 83.16 \% & 90.01 \% & 86.01 \% & 10.50 \% & 13.99 \% & 5 s / 8 cores & I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.\\
Pos-ex & & 87.75 \% & 90.75 \% & 82.54 \% & 93.67 \% & 21.78 \% & 6.33 \% & 120 ms / GPU(K20) & \\
GRES3D+SELAS & st & 87.57 \% & 90.52 \% & 85.92 \% & 89.28 \% & 16.08 \% & 10.72 \% & 110 ms / 4 core & P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.\\
CN & & 86.21 \% & 84.40 \% & 82.85 \% & 89.86 \% & 20.45 \% & 10.14 \% & 2 s / 1 core & J. Alvarez, T. Gevers, Y. LeCun and A. Lopez: Road Scene Segmentation from a Single Image. ECCV 2012 2012.\\
LWD & & 86.14 \% & 88.31 \% & 86.99 \% & 85.31 \% & 14.03 \% & 14.69 \% & 0.07 s / GPU & \\
SP-SS & & 85.07 \% & 79.86 \% & 85.97 \% & 84.20 \% & 15.11 \% & 15.80 \% & 0.01 s / 4 cores & \\
RNF & & 83.87 \% & 78.26 \% & 84.02 \% & 83.72 \% & 17.51 \% & 16.28 \% & .5 s / 1 core & \\
RES3D-Stereo & st & 83.62 \% & 85.74 \% & 79.81 \% & 87.81 \% & 24.42 \% & 12.19 \% & 0.7 s / 1 core & P. Shinzato, D. Gomes and D. Wolf: Road estimation with sparse 3D points from stereo data. Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on 2014.\\
INM & & 83.48 \% & 82.35 \% & 93.60 \% & 75.34 \% & 5.66 \% & 24.66 \% & 5 s / 1 core & \\
NSV & & 83.11 \% & 80.65 \% & 73.97 \% & 94.83 \% & 36.68 \% & 5.17 \% & 5 s / GPU & \\
SPlane & st & 82.28 \% & 82.83 \% & 76.85 \% & 88.53 \% & 29.32 \% & 11.47 \% & 2 s / 1 core & N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.\\
SPlane + BL & st & 82.04 \% & 85.56 \% & 75.11 \% & 90.39 \% & 32.93 \% & 9.61 \% & 2 s / 1 core & N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.\\
ANN & st & 80.95 \% & 68.36 \% & 69.95 \% & 96.05 \% & 45.35 \% & 3.95 \% & 3 s / 1 core & G. Vitor, D. Lima, A. Victorino and J. Ferreira: A 2D/3D Vision Based Approach Applied to Road Detection in Urban Environments. Intelligent Vehicles Symposium (IV), 2013 IEEE 2013.\\
LKW & & 75.48 \% & 78.73 \% & 65.97 \% & 88.18 \% & 50.00 \% & 11.82 \% & 0.1 s / 1 core & \\
VAP & & 71.83 \% & 60.64 \% & 62.48 \% & 84.48 \% & 55.76 \% & 15.52 \% & 1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.
\end{tabular}