\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
SNE-RoadSegV2 & & 97.55 \% & 93.98 \% & 97.57 \% & 97.53 \% & 1.34 \% & 2.47 \% & 0.03 s / GPU & \\
RD-RoTr & & 97.53 \% & 92.97 \% & 97.32 \% & 97.74 \% & 1.48 \% & 2.26 \% & 0.02 s / 1 GPU & \\
SNE-RoadSeg+ & & 97.50 \% & 93.98 \% & 97.41 \% & 97.58 \% & 1.43 \% & 2.42 \% & 0.08 s / GPU & H. Wang, R. Fan, P. Cai and M. Liu: SNE-RoadSeg+: Rethinking depth-normal translation and deep supervision for freespace detection. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021.\\
SPNet & & 97.21 \% & 93.87 \% & 97.38 \% & 97.04 \% & 1.44 \% & 2.96 \% & 0.04 s / GPU & \\
PLARD & la & 97.03 \% & 94.03 \% & 97.19 \% & 96.88 \% & 1.54 \% & 3.12 \% & 0.16 s / GPU & Z. Chen, J. Zhang and D. Tao: Progressive LiDAR adaptation for road detection. IEEE/CAA Journal of Automatica Sinica 2019.\\
HEAT & & 97.00 \% & 93.09 \% & 96.53 \% & 97.48 \% & 1.93 \% & 2.51 \% & 0.08 s / 1 core & \\
LRDNet+ & la & 96.95 \% & 92.22 \% & 96.88 \% & 97.02 \% & 1.72 \% & 2.98 \% & 0.01 s / GPU & A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.\\
HEAT & & 96.95 \% & 93.06 \% & 96.51 \% & 97.39 \% & 1.94 \% & 2.61 \% & 0.08 s / 1 core & \\
M2CRN & & 96.93 \% & 92.93 \% & 96.61 \% & 97.26 \% & 1.88 \% & 2.74 \% & 0.01 s / GPU & \\
UCENet & & 96.91 \% & 93.22 \% & 96.59 \% & 97.24 \% & 1.89 \% & 2.76 \% & 0.02 s / 1 core & \\
DUNet & & 96.89 \% & 93.22 \% & 96.57 \% & 97.21 \% & 1.90 \% & 2.79 \% & 0.02 s / 1 core & \\
USNet & & 96.89 \% & 93.25 \% & 96.51 \% & 97.27 \% & 1.94 \% & 2.73 \% & 0.02 s / GPU & Y. Chang, F. Xue, F. Sheng, W. Liang and A. Ming: Fast Road Segmentation via Uncertainty-aware Symmetric Network. IEEE International Conference on Robotics and Automation (ICRA) 2022.\\
LRDNet (L) & la & 96.87 \% & 91.91 \% & 96.73 \% & 97.01 \% & 1.81 \% & 2.99 \% & 0.1 s / GPU & A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.\\
UCENet+ & & 96.80 \% & 93.87 \% & 96.66 \% & 96.94 \% & 1.84 \% & 3.06 \% & 0.04 s / 1 core & \\
DFM-RTFNet & & 96.78 \% & 94.05 \% & 96.62 \% & 96.93 \% & 1.87 \% & 3.07 \% & 0.08 s / GPU & H. Wang, R. Fan, Y. Sun and M. Liu: Dynamic fusion module evolves drivable area and road anomaly detection: A benchmark and algorithms. IEEE Transactions on Cybernetics 2021.\\
SNE-RoadSeg & & 96.75 \% & 94.07 \% & 96.90 \% & 96.61 \% & 1.70 \% & 3.39 \% & 0.18 s / GPU & R. Fan, H. Wang, P. Cai and M. Liu: SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection. ECCV 2020.\\
LRDNet(S) & la & 96.74 \% & 92.54 \% & 96.79 \% & 96.69 \% & 1.76 \% & 3.31 \% & .009 s / GPU & A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.\\
SARNet & & 96.63 \% & 90.76 \% & 95.87 \% & 97.41 \% & 2.31 \% & 2.59 \% & 0.70 s / 1 core & \\
UCENet & & 96.62 \% & 93.84 \% & 96.49 \% & 96.75 \% & 1.94 \% & 3.25 \% & 0.04 s / GPU & \\
3MT & & 96.60 \% & 93.90 \% & 96.46 \% & 96.73 \% & 1.95 \% & 3.27 \% & 0.07 s / GPU & \\
LightFusion & & 96.53 \% & 93.87 \% & 96.03 \% & 97.03 \% & 2.21 \% & 2.97 \% & 0.027 s / GPU & \\
CLCFNet & la & 96.38 \% & 90.85 \% & 96.38 \% & 96.39 \% & 1.99 \% & 3.61 \% & 0.02 s / GPU & S. Gu, J. Yang and H. Kong: A Cascaded LiDAR-Camera Fusion Network for Road Detection. ICRA 2021.\\
RBANet & & 96.30 \% & 89.72 \% & 95.14 \% & 97.50 \% & 2.75 \% & 2.50 \% & 0.16 s / GPU & J. Sun, S. Kim, S. Lee, Y. Kim and S. Ko: Reverse and Boundary Attention Network for Road Segmentation. Proceedings of the IEEE International Conference on Computer Vision Workshops 2019.\\
LidCamNet & la & 96.03 \% & 93.93 \% & 96.23 \% & 95.83 \% & 2.07 \% & 4.17 \% & 0.15 s / GPU & L. Caltagirone, M. Bellone, L. Svensson and M. Wahde: LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks. Robotics and Autonomous Systems 2018.\\
NIM-RTFNet & & 96.02 \% & 94.01 \% & 96.43 \% & 95.62 \% & 1.95 \% & 4.38 \% & 0.05 s / GPU & H. Wang, R. Fan, Y. Sun and M. Liu: Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.\\
CLCFNet (LiDAR) & la & 95.97 \% & 90.61 \% & 96.12 \% & 95.82 \% & 2.13 \% & 4.18 \% & 0.02 s / GPU & S. Gu, J. Yang and H. Kong: A Cascaded LiDAR-Camera Fusion Network for Road Detection. ICRA 2021.\\
CRS & & 95.94 \% & 93.44 \% & 95.52 \% & 96.37 \% & 2.49 \% & 3.63 \% & 0.70 s / 1 core & \\
LC-CRF & la & 95.68 \% & 88.34 \% & 93.62 \% & 97.83 \% & 3.67 \% & 2.17 \% & 0.18 s / GPU & S. Gu, Y. Zhang, J. Tang, J. Yang and H. Kong: Road Detection through CRF based LiDAR-Camera Fusion. ICRA 2019.\\
MBRoadSeg & & 95.64 \% & 90.30 \% & 95.78 \% & 95.51 \% & 2.32 \% & 4.49 \% & 7.1ms / GPU & \\
MBRoadSeg & & 95.64 \% & 90.30 \% & 95.78 \% & 95.51 \% & 2.32 \% & 4.49 \% & .007 s / GPU & \\
RGB36-Cotrain & & 95.55 \% & 93.71 \% & 95.68 \% & 95.42 \% & 2.37 \% & 4.58 \% & 0.1 s / 1 core & L. Caltagirone, S. Lennart, M. Wahde and M. Sanfridson: Lidar-Camera Co-Training for Semi- Supervised Road Detection. arXiv preprint arXiv:1911.12597 2019.\\
SSLGAN & & 95.53 \% & 90.35 \% & 95.84 \% & 95.24 \% & 2.28 \% & 4.76 \% & 700ms / GPU & X. Han, J. Lu, C. Zhao, S. You and H. Li: Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks. IEEE Signal Processing Letters 2018.\\
TVFNet & la & 95.34 \% & 90.26 \% & 95.73 \% & 94.94 \% & 2.33 \% & 5.06 \% & 0.04 s / GPU & S. Gu, Y. Zhang, J. Yang, J. Alvarez and H. Kong: Two-View Fusion based Convolutional Neural Network for Urban Road Detection. IROS 2019.\\
LFD-RoadSeg & & 95.21 \% & 93.71 \% & 95.35 \% & 95.08 \% & 2.56 \% & 4.92 \% & .004 s / GPU & \\
RBNet & & 94.97 \% & 91.49 \% & 94.94 \% & 95.01 \% & 2.79 \% & 4.99 \% & 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.\\
BJN & & 94.89 \% & 90.63 \% & 96.14 \% & 93.67 \% & 2.07 \% & 6.33 \% & 0.02 s / 1 core & B. Yu, D. Lee, J. Lee and S. Kee: Free Space Detection Using Camera-LiDAR Fusion in a Bird’s Eye View Plane. Sensors 2021.\\
StixelNet II & & 94.88 \% & 87.75 \% & 92.97 \% & 96.87 \% & 4.04 \% & 3.13 \% & 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.\\
MultiNet & & 94.88 \% & 93.71 \% & 94.84 \% & 94.91 \% & 2.85 \% & 5.09 \% & 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.\\
Hadamard-FCN & & 94.85 \% & 91.48 \% & 94.81 \% & 94.89 \% & 2.86 \% & 5.11 \% & 0.02 s / GPU & M. Oeljeklaus: An Integrated Approach for Traffic Scene Understanding from Monocular Cameras. 2021.\\
HA-DeepLabv3+ & & 94.83 \% & 93.24 \% & 94.77 \% & 94.89 \% & 2.88 \% & 5.11 \% & 0.06 s / 1 core & R. Fan, H. Wang, P. Cai, J. Wu, M. Bocus, L. Qiao and M. Liu: Learning collision-free space detection from stereo images: Homography matrix brings better data augmentation. IEEE/ASME Transactions on Mechatronics 2021.\\
DTN-DSC & & 94.73 \% & 93.51 \% & 94.63 \% & 94.84 \% & 2.97 \% & 5.16 \% & 11m s / GPU & \\
TEDNet & la & 94.62 \% & 93.05 \% & 94.28 \% & 94.96 \% & 3.17 \% & 5.04 \% & 0.09 s / GPU & M. Bayón-Gutiérrez, J. Benítez- Andrades, S. Rubio-Martín, J. Aveleira-Mata, H. Alaiz-Moretón and M. García-Ordás: Roadway Detection Using Convolutional Neural Network Through Camera and LiDAR Data. Hybrid Artificial Intelligent Systems 2022.\\
RoadNet3 & & 94.44 \% & 93.45 \% & 94.69 \% & 94.18 \% & 2.91 \% & 5.82 \% & 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.\\
CLRD & la & 94.20 \% & 92.66 \% & 94.25 \% & 94.14 \% & 3.16 \% & 5.86 \% & 0.05 s / GPU & M. Bayón-Gutiérrez, J. Benítez- Andrades, S. Rubio-Martín, J. Aveleira-Mata, H. Alaiz-Moretón and M. García-Ordás: Roadway Detection Using Convolutional Neural Network Through Camera and LiDAR Data. Hybrid Artificial Intelligent Systems 2022.\\
LoDNN & la & 94.07 \% & 92.03 \% & 92.81 \% & 95.37 \% & 4.07 \% & 4.63 \% & 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.\\
ChipNet & la & 94.05 \% & 88.29 \% & 93.57 \% & 94.53 \% & 3.58 \% & 5.47 \% & 12 ms / GPU & Y. Lyu, L. Bai and X. Huang: ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA. IEEE Transactions on Circuits and Systems I: Regular Papers 2019.\\
DEEP-DIG & & 93.98 \% & 93.65 \% & 94.26 \% & 93.69 \% & 3.14 \% & 6.31 \% & 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.\\
Up-Conv-Poly & & 93.83 \% & 90.47 \% & 94.00 \% & 93.67 \% & 3.29 \% & 6.33 \% & 0.08 s / GPU & G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.\\
OFA Net & & 93.74 \% & 85.37 \% & 90.36 \% & 97.38 \% & 5.72 \% & 2.62 \% & 0.04 s / GPU & S. Zhang, Z. Zhang, L. Sun and W. Qin: One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation. Applied Sciences 2019.\\
DDN & & 93.43 \% & 89.67 \% & 95.09 \% & 91.82 \% & 2.61 \% & 8.18 \% & 2 s / GPU & R. Mohan: Deep Deconvolutional Networks for Scene Parsing. 2014.\\
HID-LS & la & 93.11 \% & 87.33 \% & 92.52 \% & 93.71 \% & 4.18 \% & 6.29 \% & 0.25 s / 1 cores & S. Gu, Y. Zhang, J. Yang and H. Kong: Lidar-based urban road detection by histograms of normalized inverse depths and line scanning. ECMR 2017.S. Gu, Y. Zhang, X. Yuan, J. Yang, T. Wu and H. Kong: Histograms of the Normalized Inverse Depth and Line Scanning for Urban Road Detection. IEEE Trans. Intelligent Transportation Systems 2019.\\
RGB36-Super & & 92.94 \% & 92.29 \% & 93.14 \% & 92.74 \% & 3.77 \% & 7.26 \% & 0.1 s / 1 core & L. Caltagirone, S. Lennart, M. Wahde and M. Sanfridson: Lidar-Camera Co-Training for Semi- Supervised Road Detection. arXiv preprint arXiv:1911.12597 2019.\\
RoadNet-RT & & 92.55 \% & 93.21 \% & 92.94 \% & 92.16 \% & 3.86 \% & 7.84 \% & 8m s / GPU & L. Bai, Y. Lyu and X. Huang: RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation. arXiv preprint arXiv:2006.07644 2020.\\
Up-Conv & & 92.39 \% & 90.24 \% & 93.03 \% & 91.76 \% & 3.79 \% & 8.24 \% & 0.05 s / GPU & G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.\\
ALO-AVG-MM & & 92.03 \% & 85.64 \% & 90.65 \% & 93.45 \% & 5.31 \% & 6.55 \% & 0.0296 sec / & F. Reis, R. Almeida, E. Kijak, S. Malinowski, S. Guimaraes and Z. Jr.: Combining convolutional side-outputs for road image segmentation. 2019 International Joint Conference on Neural Networks (IJCNN) - \textbfAccepted 2019.\\
FTP & & 91.61 \% & 90.96 \% & 91.04 \% & 92.20 \% & 5.00 \% & 7.80 \% & 0.28 s / GPU & A. Laddha, M. Kocamaz, L. Navarro-Serment and M. Hebert: Map-Supervised Road Detection. IEEE Intelligent Vehicles Symposium Proceedings 2016.\\
HybridCRF & la & 90.81 \% & 86.01 \% & 91.05 \% & 90.57 \% & 4.90 \% & 9.43 \% & 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 2018.\\
FCN-LC & & 90.79 \% & 85.83 \% & 90.87 \% & 90.72 \% & 5.02 \% & 9.28 \% & 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.\\
LidarHisto & la & 90.67 \% & 84.79 \% & 93.06 \% & 88.41 \% & 3.63 \% & 11.59 \% & 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.\\
HIM & & 90.64 \% & 81.42 \% & 91.62 \% & 89.68 \% & 4.52 \% & 10.32 \% & 7 s / >8 cores & D. Munoz, J. Bagnell and M. Hebert: Stacked Hierarchical Labeling. European Conference on Computer Vision (ECCV) 2010.\\
MixedCRF & la & 90.59 \% & 84.24 \% & 89.11 \% & 92.13 \% & 6.20 \% & 7.87 \% & 6s / 1 core & X. Han, H. Wang, J. Lu and C. Zhao: Road detection based on the fusion of Lidar and image data. 2017.\\
BMCF & st & 89.75 \% & 84.15 \% & 89.02 \% & 90.49 \% & 6.15 \% & 9.51 \% & 2.5 s / 1 core & L. Wang, T. Wu, Z. Xiao, L. Xiao, D. Zhao and J. Han: Multi-cue road boundary detection using stereo vision. 2016 IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2016.\\
NNP & st & 89.68 \% & 86.50 \% & 89.67 \% & 89.68 \% & 5.69 \% & 10.32 \% & 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.\\
StixelNet & & 89.12 \% & 81.23 \% & 85.80 \% & 92.71 \% & 8.45 \% & 7.29 \% & 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.\\
CB & & 88.97 \% & 79.69 \% & 89.50 \% & 88.44 \% & 5.71 \% & 11.56 \% & 2 s / 1 core & C. Mendes, V. Frémont and D. Wolf: Vision-Based Road Detection using Contextual Blocks. 2015.\\
FusedCRF & la & 88.25 \% & 79.24 \% & 83.62 \% & 93.44 \% & 10.08 \% & 6.56 \% & 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.\\
MAP & & 87.80 \% & 89.96 \% & 86.01 \% & 89.66 \% & 8.04 \% & 10.34 \% & 0.28s / & A. Laddha, M. Kocamaz, L. Navarro-Serment and M. Hebert: Map-Supervised Road Detection. IEEE Intelligent Vehicles Symposium Proceedings 2016.\\
ProbBoost & st & 87.78 \% & 77.30 \% & 86.59 \% & 89.01 \% & 7.60 \% & 10.99 \% & 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.\\
SPRAY & & 87.09 \% & 91.12 \% & 87.10 \% & 87.08 \% & 7.10 \% & 12.92 \% & 45 ms / & T. Kuehnl, F. Kummert and J. Fritsch: Spatial Ray Features for Real-Time Ego-Lane Extraction. Proc. IEEE Intelligent Transportation Systems 2012.\\
multi-task CNN & & 86.81 \% & 82.15 \% & 78.26 \% & 97.47 \% & 14.92 \% & 2.53 \% & 25.1 ms / GPU & M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.\\
RES3D-Velo & la & 86.58 \% & 78.34 \% & 82.63 \% & 90.92 \% & 10.53 \% & 9.08 \% & 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.\\
GRES3D+VELO & la & 86.07 \% & 84.34 \% & 82.16 \% & 90.38 \% & 10.81 \% & 9.62 \% & 60 ms / 4 cores & P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.\\
PGM-ARS & & 85.69 \% & 73.83 \% & 82.34 \% & 89.33 \% & 10.56 \% & 10.67 \% & 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.\\
geo+gpr+crf & st & 85.56 \% & 74.21 \% & 82.81 \% & 88.50 \% & 10.12 \% & 11.50 \% & 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+SELAS & st & 85.09 \% & 86.86 \% & 82.27 \% & 88.10 \% & 10.46 \% & 11.90 \% & 110 ms / 4 core & P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.\\
SCRFFPFHGSP & st & 84.93 \% & 76.31 \% & 85.37 \% & 84.49 \% & 7.98 \% & 15.51 \% & 5 s / 8 cores & I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.\\
HistonBoost & st & 83.92 \% & 73.75 \% & 82.24 \% & 85.66 \% & 10.19 \% & 14.34 \% & 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.\\
BM & st & 83.47 \% & 72.23 \% & 75.90 \% & 92.72 \% & 16.22 \% & 7.28 \% & 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.\\
SRF & & 82.44 \% & 87.37 \% & 80.60 \% & 84.36 \% & 11.18 \% & 15.64 \% & 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-Stereo & st & 81.08 \% & 81.68 \% & 78.14 \% & 84.24 \% & 12.98 \% & 15.76 \% & 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.\\
ARSL-AMI & & 80.36 \% & 70.23 \% & 83.24 \% & 77.67 \% & 8.61 \% & 22.33 \% & 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.\\
SPlane + BL & st & 79.63 \% & 83.90 \% & 72.59 \% & 88.17 \% & 18.34 \% & 11.83 \% & 2 s / 1 core & N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.\\
CN & & 79.02 \% & 78.80 \% & 76.64 \% & 81.55 \% & 13.69 \% & 18.45 \% & 2 s / 1 core & J. Alvarez, T. Gevers, Y. LeCun and A. Lopez: Road Scene Segmentation from a Single Image. ECCV 2012 2012.\\
SPlane & st & 78.69 \% & 77.16 \% & 71.96 \% & 86.80 \% & 18.63 \% & 13.20 \% & 2 s / 1 core & N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.\\
ANN & st & 67.70 \% & 52.50 \% & 54.19 \% & 90.17 \% & 41.98 \% & 9.83 \% & 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.
\end{tabular}