The KITTI Vision Benchmark Suite

This benchmark has been created in collaboration with Jannik Fritsch and Tobias Kuehnl from Honda Research Institute Europe GmbH. The road and lane estimation benchmark consists of 289 training and 290 test images. It contains three different categories of road scenes:

• uu - urban unmarked (98/100)
• um - urban marked (95/96)
• umm - urban multiple marked lanes (96/94)
• urban - combination of the three above

Ground truth has been generated by manual annotation of the images and is available for two different road terrain types: road - the road area, i.e, the composition of all lanes, and lane - the ego-lane, i.e., the lane the vehicle is currently driving on (only available for category "um"). Ground truth is provided for training images only.

• Download base kit with: left color images, calibration and training labels (0.5 GB)
• Download right color image extension (0.5 GB)
• Download grayscale image extension (0.3 GB)
• Download Velodyne laser point extension (1 GB)
• Download OXTS GPS/IMU extension (1 MB)
• Download development kit (1 MB)
• Mapping of training set to raw data sequences (1 MB)

We evaluate road and lane estimation performance in the bird's-eye-view space. For the classical pixel-based evaluation we use established measures as discussed in our ITSC 2013 publication. MaxF: Maximum F1-measure, AP: Average precision as used in PASCAL VOC challenges, PRE: Precision, REC: Recall, FPR: False Positive Rate, FNR: False Negative Rate (the four latter measures are evaluated at the working point MaxF), F1: F1 score, HR: Hit rate. For the novel behavior-based evaluation a corridor with the vehicle width (2.2m) is fitted to the lane estimation processing result and evaluation is performed for 3 different distance values: 20 m, 30 m, and 40 m. We refer to our ITSC 2013 publication for more details.
IMPORTANT NOTE: On 09.02.2015 we have improved the accuracy of the ground truth and re-calculated the results for all methods. Please download the devkit and the dataset with the improved ground truth for training again, if you have downloaded the files prior to 09.02.2015. Please consider reporting these new number for all future submissions. The last leaderboards right before the changes can be found here!

UM_ROAD

	Method	Setting	Code	MaxF	AP	PRE	REC	FPR	FNR	Runtime	Environment
1	UNV			96.69 %	92.41 %	97.38 %	96.01 %	1.18 %	3.99 %	1.2 s	GPU @ 3.0 Ghz (Python + C/C++)
2	iDST-VT			96.66 %	93.65 %	96.55 %	96.76 %	1.57 %	3.24 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
3	VGGFCN-6D			96.64 %	92.85 %	96.37 %	96.91 %	1.66 %	3.09 %	.006 s	GPU @ 3.5 Ghz (Python)
4	NF2CNN			96.09 %	88.40 %	94.11 %	98.16 %	2.80 %	1.84 %	.006 s	GPU @ 3.5 Ghz (Python)
5	KRSF			96.02 %	93.60 %	95.61 %	96.44 %	2.02 %	3.56 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
6	KRS			95.89 %	93.51 %	95.79 %	95.99 %	1.92 %	4.01 %	0.3 s	GPU @ 2.5 Ghz (Python)
7	YhY		code	95.80 %	89.11 %	94.89 %	96.73 %	2.38 %	3.27 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
8	LidCamNet			95.62 %	93.54 %	95.77 %	95.48 %	1.92 %	4.52 %	0.15 s	GPU @ 2.5 Ghz (Python)
9	DFFA			95.58 %	89.30 %	95.10 %	96.06 %	2.25 %	3.94 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
10	MVnet			95.45 %	91.49 %	97.51 %	93.49 %	1.09 %	6.51 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
11	RSNet			95.28 %	92.43 %	95.22 %	95.35 %	2.18 %	4.65 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
12	WNet			95.24 %	93.04 %	96.01 %	94.48 %	1.79 %	5.52 %	0.1 s	4 cores @ 2.5 Ghz (Python)
13	BIRD			95.18 %	92.44 %	94.69 %	95.68 %	2.45 %	4.32 %	25 ms	GPU @ 2.5 Ghz (Python + C/C++)
14	TDCac1 CNN			94.86 %	89.62 %	95.45 %	94.28 %	2.05 %	5.72 %	.093 s	1 core @ 1.0 Ghz (C/C++)
15	RSNet-			94.84 %	92.83 %	94.32 %	95.37 %	2.62 %	4.63 %	0.07 s	GPU @ 2.5 Ghz (Python)
16	baseline			94.80 %	92.80 %	94.35 %	95.25 %	2.60 %	4.75 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
17	IDA-Fusion			94.77 %	88.03 %	93.71 %	95.86 %	2.93 %	4.14 %	0.1 s	4 cores @ 3.5 Ghz (C/C++)
18	RBNet			94.77 %	91.42 %	95.16 %	94.37 %	2.19 %	5.63 %	0.18 s	GPU @ 2.5 Ghz (Matlab + C/C++)
Z. Chen and Z. Chen: RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection. International Conference on Neural Information Processing 2017.
19	WSLGAN			94.73 %	89.22 %	95.01 %	94.45 %	2.26 %	5.55 %	800ms	GPU @ 1.5 Ghz (Python)
20	MMN			94.72 %	92.51 %	94.84 %	94.60 %	2.34 %	5.40 %	0.1 s	GPU @ 2.5 Ghz (C/C++)
21	KRS			94.69 %	93.40 %	94.72 %	94.67 %	2.41 %	5.33 %	1 s	GPU @ 2.5 Ghz (Python)
22	RSNet2			94.65 %	92.54 %	94.45 %	94.85 %	2.54 %	5.15 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
23	SSLGAN			94.62 %	89.50 %	95.32 %	93.93 %	2.10 %	6.07 %	700ms	GPU @ 1.5 Ghz (Python)
24	FNETMS			94.51 %	92.72 %	94.92 %	94.11 %	2.30 %	5.89 %	0.04 s	GPU @ 2.0 Ghz (Python + C/C++)
25	DEEP-DIG			94.16 %	93.41 %	95.02 %	93.32 %	2.23 %	6.68 %	0.14 s	GPU @ 3.5 Ghz (Python + C/C++)
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.
26	FusionNet			94.15 %	92.26 %	95.18 %	93.14 %	2.15 %	6.86 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
27	SPN			94.06 %	89.64 %	95.47 %	92.68 %	2.00 %	7.32 %	1 s	1 core @ 2.5 Ghz (C/C++)
28	StixelNet II			94.05 %	85.85 %	91.30 %	96.98 %	4.21 %	3.02 %	1.2 s	1 core @ 3.0 Ghz (Matlab + C/C++)
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.
29	MultiNet		code	93.99 %	93.24 %	94.51 %	93.48 %	2.47 %	6.52 %	0.17 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Teichmann, M. Weber, J. Zoellner, R. Cipolla and R. Urtasun: MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving. CoRR 2016.
30	FDN			93.99 %	93.29 %	94.53 %	93.46 %	2.47 %	6.54 %	0.2 s	GPU 1 core @ 2.5 Ghz (Python)
31	CoDNN			93.90 %	92.86 %	94.51 %	93.29 %	2.47 %	6.71 %	0.01 s	1 core @ 2.5 Ghz (Python + C/C++)
32	FuseNet			93.86 %	93.34 %	94.49 %	93.23 %	2.48 %	6.77 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
33	FCN-GCBs			93.86 %	86.62 %	92.15 %	95.62 %	3.71 %	4.38 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
34	ResNetPK			93.78 %	89.01 %	94.78 %	92.81 %	2.33 %	7.19 %	0.4s	GPU @ 1.5 Ghz (Python)
35	FCN_RGBD			93.78 %	93.40 %	94.71 %	92.86 %	2.36 %	7.14 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
36	MBN			93.77 %	85.59 %	91.02 %	96.69 %	4.34 %	3.31 %	0.16 s	GPU @ 2.5 Ghz (Python)
37	DDN			93.65 %	88.55 %	94.28 %	93.03 %	2.57 %	6.97 %	2 s	GPU @ 2.5 Ghz (Python + C/C++)
R. Mohan: Deep Deconvolutional Networks for Scene Parsing. 2014.
38	wt			93.27 %	92.92 %	93.20 %	93.34 %	3.10 %	6.66 %	0.1 s	GPU @ 1.0 Ghz (Python)
39	RDSN			92.77 %	87.54 %	93.16 %	92.39 %	3.09 %	7.61 %	0.25 s	GPU @ 2.5 Ghz (Python)
40	LoDNN			92.75 %	89.98 %	90.09 %	95.58 %	4.79 %	4.42 %	18 ms	GPU @ 2.5 Ghz (Torch)
L. Caltagirone, S. Scheidegger, L. Svensson and M. Wahde: Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks. IEEE Intelligent Vehicles Symposium 2017.
41	SUNet			92.63 %	86.51 %	92.03 %	93.24 %	3.68 %	6.76 %	0.018s
42	DFN			92.32 %	88.26 %	91.79 %	92.85 %	3.78 %	7.15 %	0.25 s	GPU @ >3.5 Ghz (Python)
43	RSNetVGG			92.26 %	92.51 %	93.92 %	90.65 %	2.67 %	9.35 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
44	Up-Conv-Poly		code	92.20 %	88.85 %	92.57 %	91.83 %	3.36 %	8.17 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.
45	HID-LS			92.19 %	84.15 %	89.44 %	95.12 %	5.12 %	4.88 %	0.25 s	4 cores @ 3.0 Ghz (C/C++)
46	MixedCRF			91.57 %	84.68 %	90.02 %	93.19 %	4.71 %	6.81 %	6s	1 core @ 2.5 Ghz (Matlab + C/C++)
X. Han, H. Wang, J. Lu and C. Zhao: Road detection based on the fusion of Lidar and image data. 2017.
47	LiDAR-SPHnet			91.50 %	81.98 %	87.05 %	96.42 %	6.53 %	3.58 %	0.14 s	GPU @ 1.5 Ghz (Matlab)
48	fcn_rgbd			91.25 %	92.30 %	89.86 %	92.68 %	4.77 %	7.32 %	0.6 s	1 core @ 2.5 Ghz (C/C++)
49	FTP			91.20 %	90.60 %	91.11 %	91.29 %	4.06 %	8.71 %	0.28 s	GPU @ 2.5 Ghz (C/C++)
A. Laddha, M. Kocamaz, L. Navarro-Serment and M. Hebert: Map-Supervised Road Detection. IEEE Intelligent Vehicles Symposium Proceedings 2016.
50	HybridCRF			90.99 %	85.26 %	90.65 %	91.33 %	4.29 %	8.67 %	1.5 s	1 core @ 2.5 Ghz (C/C++)
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.
51	NNP			90.50 %	87.95 %	91.43 %	89.59 %	3.83 %	10.41 %	5 s	4 cores @ 2.5 Ghz (Matlab)
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.
52	Up-Conv			90.48 %	88.20 %	91.30 %	89.67 %	3.90 %	10.33 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.
53	TFSeg			90.12 %	89.85 %	88.00 %	92.36 %	5.74 %	7.64 %	0.07 s	GPU @ 1.0 Ghz (Python)
54	HIM			90.07 %	79.98 %	90.79 %	89.35 %	4.13 %	10.65 %	7 s	>8 cores @ 2.5 Ghz (Python + C/C++)
D. Munoz, J. Bagnell and M. Hebert: Stacked Hierarchical Labeling. European Conference on Computer Vision (ECCV) 2010.
55	LidarHisto		code	89.87 %	83.03 %	91.28 %	88.49 %	3.85 %	11.51 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
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.
56	CNN-FCRF			89.87 %	83.38 %	88.58 %	91.18 %	5.35 %	8.82 %	1 s	4 cores @ 3.5 Ghz (C/C++)
57	FusedCRF			89.55 %	80.00 %	84.87 %	94.78 %	7.70 %	5.22 %	2 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, B. Dai, D. Liu, T. Hu and T. Wu: CRF based Road Detection with Multi-Sensor Fusion. Intelligent Vehicles Symposium (IV) 2015.
58	LWDS			89.50 %	86.11 %	90.59 %	88.44 %	4.19 %	11.56 %	0.07 s	GPU @ 2.5 Ghz (Python)
59	FCN-LC			89.36 %	78.80 %	89.35 %	89.37 %	4.85 %	10.63 %	0.03 s	GPU Titan X
C. Mendes, V. Frémont and D. Wolf: Exploiting Fully Convolutional Neural Networks for Fast Road Detection. IEEE Conference on Robotics and Automation (ICRA) 2016.
60	CB			88.89 %	82.17 %	87.26 %	90.58 %	6.03 %	9.42 %	2 s	1 core @ 3.4 Ghz (Python) + GPU
C. Mendes, V. Frémont and D. Wolf: Vision-Based Road Detection using Contextual Blocks. 2015.
61	RD			88.87 %	82.04 %	93.31 %	84.83 %	2.77 %	15.17 %	4 s	1 core @ 3.0 Ghz (Matlab + C/C++)
62	HFM			88.83 %	80.33 %	85.23 %	92.75 %	7.32 %	7.25 %	5 s	2 cores @ 2.0 Ghz (C/C++)
63	DVFCN			88.64 %	91.37 %	89.20 %	88.10 %	4.86 %	11.90 %	0.07 s	GPU @ 2.5 Ghz (Python)
64	ResAXN			88.46 %	91.06 %	90.10 %	86.88 %	4.35 %	13.12 %	0.06 s	GPU @ 1.5 Ghz (Python)
65	SPRAY			88.14 %	91.24 %	88.60 %	87.68 %	5.14 %	12.32 %	45 ms	NVIDIA GTX 580 (Python + OpenCL)
T. Kuehnl, F. Kummert and J. Fritsch: Spatial Ray Features for Real-Time Ego-Lane Extraction. Proc. IEEE Intelligent Transportation Systems 2012.
66	ProbBoost			87.48 %	80.13 %	85.02 %	90.09 %	7.23 %	9.91 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
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.
67	LWD			87.39 %	88.85 %	84.99 %	89.92 %	7.24 %	10.08 %	0.07 s	GPU @ 2.5 Ghz (Python)
68	MAP			87.33 %	89.62 %	85.77 %	88.95 %	6.73 %	11.05 %	0.28s	GPU
A. Laddha, M. Kocamaz, L. Navarro-Serment and M. Hebert: Map-Supervised Road Detection. IEEE Intelligent Vehicles Symposium Proceedings 2016.
69	Pos-ex		code	86.74 %	90.43 %	88.09 %	85.44 %	5.26 %	14.56 %	120 ms	GPU(K20) @ 0.7 Ghz (Matlab + Caffe)
70	CN24			86.32 %	89.19 %	87.80 %	84.89 %	5.37 %	15.11 %	30 s	>8 cores @ 2.5 Ghz (C/C++)
C. Brust, S. Sickert, M. Simon, E. Rodner and J. Denzler: Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding. VISAPP 2015 - Proceedings of the 10th International Conference on Computer Vision Theory and Applications, Berlin, Germany, 11-14 March, 2015 2015.
71	GRES3D+VELO			85.43 %	83.04 %	82.69 %	88.37 %	8.43 %	11.63 %	60 ms	4 cores @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
72	StixelNet			85.33 %	72.14 %	81.21 %	89.89 %	9.48 %	10.11 %	1 s	GPU @ 2.5 Ghz (C/C++)
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.
73	SPlane + BL			85.23 %	88.66 %	83.43 %	87.12 %	7.89 %	12.88 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
74	CNN+BiGRU			85.21 %	79.80 %	90.57 %	80.44 %	3.82 %	19.56 %	20 ms	GPU @ GTX950M (Python +Tensorflow)
75	geo+gpr+crf			85.13 %	72.24 %	81.33 %	89.29 %	9.34 %	10.71 %	30 s	1 core @ 2.0 Ghz (C/C++)
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.
76	CNN_LSTM			85.09 %	78.98 %	89.57 %	81.03 %	4.30 %	18.97 %	20 ms	GPU @ GTX950M (Python +Tensorflow)
77	Strait			84.28 %	87.89 %	83.66 %	84.91 %	7.56 %	15.09 %	69 ms	GPU @ K20
78	RES3D-Velo			83.81 %	73.95 %	78.56 %	89.80 %	11.16 %	10.20 %	0.36 s	1 core @ 2.5 Ghz (C/C++)
P. Shinzato, D. Wolf and C. Stiller: Road Terrain Detection: Avoiding Common Obstacle Detection Assumptions Using Sensor Fusion. Intelligent Vehicles Symposium (IV) 2014.
79	SCRFFPFHGSP			83.73 %	72.89 %	82.13 %	85.39 %	8.47 %	14.61 %	5 s	8 cores @ 2.5 Ghz (C/C++, Matlab)
I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.
80	GRES3D+SELAS			83.69 %	84.61 %	78.31 %	89.88 %	11.35 %	10.12 %	110 ms	4 core @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
81	HistonBoost			83.68 %	72.79 %	82.01 %	85.42 %	8.54 %	14.58 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
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.
82	FRS_SP			83.22 %	72.94 %	77.11 %	90.39 %	12.23 %	9.61 %	0.21 s	4 cores @ 3.0 Ghz (Matlab + C/C++)
83	SegNet			82.17 %	76.46 %	84.03 %	80.40 %	6.97 %	19.60 %	0.01 s	8 cores @ 2.5 Ghz (C/C++)
84	LKW			82.01 %	85.26 %	78.83 %	85.46 %	10.46 %	14.54 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
85	SP-SS			81.60 %	69.62 %	78.13 %	85.40 %	10.89 %	14.60 %	0.01 s	4 cores @ 3.0 Ghz (Matlab + C/C++)
86	PGM-ARS			80.97 %	69.11 %	77.51 %	84.76 %	11.21 %	15.24 %	0.05 s	i74700MQ @ 2.1Ghz (C/C++)
M. Passani, J. Yebes and L. Bergasa: Fast Pixelwise Road Inference Based on Uniformly Reweighted Belief Propagation . Proc. IEEE Intelligent Vehicles Symposium 2015.
87	RES3D-Stereo			78.98 %	80.06 %	75.94 %	82.27 %	11.88 %	17.73 %	0.7 s	1 core @ 2.5 Ghz (C/C++)
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.
88	BM			78.90 %	66.06 %	69.53 %	91.19 %	18.21 %	8.81 %	2 s	2 cores @ 2.5 Ghz (Matlab)
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.
89	CNN_LSTM			78.42 %	69.11 %	83.28 %	74.09 %	6.78 %	25.91 %	54 ms	GPU @ 2.0 Ghz (Python + C/C++)
90	SPlane			78.19 %	76.41 %	72.03 %	85.50 %	15.13 %	14.50 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
91	SRF			76.43 %	83.24 %	75.53 %	77.35 %	11.42 %	22.65 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, B. Dai, D. Liu, D. Zhao and T. Wu: Monocular Road Detection Using Structured Random Forest. Int J Adv Robot Syst 2016.
92	CN24			76.28 %	79.29 %	72.44 %	80.55 %	13.96 %	19.45 %	20 s	>8 cores @ 2.5 Ghz (C/C++)
C. Brust, S. Sickert, M. Simon, E. Rodner and J. Denzler: Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding. VISAPP 2015 - Proceedings of the 10th International Conference on Computer Vision Theory and Applications, Berlin, Germany, 11-14 March, 2015 2015.
93	CN			73.69 %	76.68 %	69.18 %	78.83 %	16.00 %	21.17 %	2 s	1 core @ 2.5 Ghz (C/C++)
J. Alvarez, T. Gevers, Y. LeCun and A. Lopez: Road Scene Segmentation from a Single Image. ECCV 2012 2012.
94	ARSL-AMI			71.97 %	61.04 %	78.03 %	66.79 %	8.57 %	33.21 %	0.05 s	4 cores @ 2.5 Ghz (C/C++)
M. Passani, J. Yebes and L. Bergasa: CRF-based semantic labeling in miniaturized road scenes . Proc. IEEE Intelligent Transportation Systems 2014.
95	ANN			62.83 %	46.77 %	50.21 %	83.91 %	37.91 %	16.09 %	3 s	1 core @ 3.0 Ghz (C/C++)
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.
96	VAP			59.23 %	42.05 %	44.44 %	88.75 %	50.55 %	11.25 %	1 s	1 core @ 2.5 Ghz (Matlab)
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Table as LaTeX | Only published Methods

UMM_ROAD

	Method	Setting	Code	MaxF	AP	PRE	REC	FPR	FNR	Runtime	Environment
1	iDST-VT			97.87 %	95.60 %	97.99 %	97.74 %	2.20 %	2.26 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
2	VGGFCN-6D			97.78 %	95.34 %	97.76 %	97.80 %	2.47 %	2.20 %	.006 s	GPU @ 3.5 Ghz (Python)
3	NF2CNN			97.77 %	93.31 %	97.41 %	98.13 %	2.87 %	1.87 %	.006 s	GPU @ 3.5 Ghz (Python)
4	YhY		code	97.42 %	93.08 %	97.15 %	97.68 %	3.15 %	2.32 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
5	KRSF			97.34 %	95.58 %	97.42 %	97.26 %	2.83 %	2.74 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
6	UNV			97.34 %	94.23 %	97.52 %	97.16 %	2.71 %	2.84 %	1.2 s	GPU @ 3.0 Ghz (Python + C/C++)
7	KRS			97.27 %	95.55 %	97.19 %	97.34 %	3.09 %	2.66 %	0.3 s	GPU @ 2.5 Ghz (Python)
8	DFFA			97.26 %	92.75 %	96.79 %	97.74 %	3.56 %	2.26 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
9	BIRD			97.10 %	95.31 %	96.64 %	97.56 %	3.73 %	2.44 %	25 ms	GPU @ 2.5 Ghz (Python + C/C++)
10	LidCamNet			97.08 %	95.51 %	97.28 %	96.88 %	2.98 %	3.12 %	0.15 s	GPU @ 2.5 Ghz (Python)
11	MVnet			96.99 %	93.94 %	98.10 %	95.90 %	2.04 %	4.10 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
12	WSLGAN			96.95 %	92.87 %	96.92 %	96.98 %	3.39 %	3.02 %	800ms	GPU @ 1.5 Ghz (Python)
13	baseline			96.88 %	95.39 %	96.36 %	97.40 %	4.04 %	2.60 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
14	RSNet			96.85 %	95.26 %	96.79 %	96.91 %	3.54 %	3.09 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
15	KRS			96.84 %	95.49 %	96.70 %	96.98 %	3.64 %	3.02 %	1 s	GPU @ 2.5 Ghz (Python)
16	SPN			96.81 %	92.89 %	96.94 %	96.67 %	3.36 %	3.33 %	1 s	1 core @ 2.5 Ghz (C/C++)
17	SSLGAN			96.72 %	92.99 %	97.05 %	96.40 %	3.22 %	3.60 %	700ms	GPU @ 1.5 Ghz (Python)
18	WNet			96.63 %	94.96 %	96.66 %	96.60 %	3.67 %	3.40 %	0.1 s	4 cores @ 2.5 Ghz (Python)
19	RSNet2			96.60 %	95.28 %	96.57 %	96.63 %	3.78 %	3.37 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
20	CoDNN			96.56 %	95.33 %	96.41 %	96.71 %	3.96 %	3.29 %	0.01 s	1 core @ 2.5 Ghz (Python + C/C++)
21	RSNet-			96.40 %	95.34 %	96.59 %	96.22 %	3.74 %	3.78 %	0.07 s	GPU @ 2.5 Ghz (Python)
22	IDA-Fusion			96.37 %	92.66 %	96.69 %	96.06 %	3.61 %	3.94 %	0.1 s	4 cores @ 3.5 Ghz (C/C++)
23	StixelNet II			96.22 %	91.24 %	95.13 %	97.33 %	5.48 %	2.67 %	1.2 s	1 core @ 3.0 Ghz (Matlab + C/C++)
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.
24	FDN			96.19 %	95.26 %	95.49 %	96.89 %	5.03 %	3.11 %	0.2 s	GPU 1 core @ 2.5 Ghz (Python)
25	MultiNet		code	96.15 %	95.36 %	95.79 %	96.51 %	4.67 %	3.49 %	0.17 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Teichmann, M. Weber, J. Zoellner, R. Cipolla and R. Urtasun: MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving. CoRR 2016.
26	MMN			96.15 %	94.98 %	96.07 %	96.22 %	4.33 %	3.78 %	0.1 s	GPU @ 2.5 Ghz (C/C++)
27	MBN			96.14 %	91.43 %	95.33 %	96.96 %	5.22 %	3.04 %	0.16 s	GPU @ 2.5 Ghz (Python)
28	wt			96.11 %	95.44 %	96.08 %	96.14 %	4.31 %	3.86 %	0.1 s	GPU @ 1.0 Ghz (Python)
29	TDCac1 CNN			96.11 %	92.62 %	96.65 %	95.57 %	3.64 %	4.43 %	.093 s	1 core @ 1.0 Ghz (C/C++)
30	FNETMS			96.08 %	94.95 %	95.56 %	96.60 %	4.93 %	3.40 %	0.04 s	GPU @ 2.0 Ghz (Python + C/C++)
31	RBNet			96.06 %	93.49 %	95.80 %	96.31 %	4.64 %	3.69 %	0.18 s	GPU @ 2.5 Ghz (Matlab + C/C++)
Z. Chen and Z. Chen: RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection. International Conference on Neural Information Processing 2017.
32	LoDNN			96.05 %	95.03 %	95.79 %	96.31 %	4.66 %	3.69 %	18 ms	GPU @ 2.5 Ghz (Torch)
L. Caltagirone, S. Scheidegger, L. Svensson and M. Wahde: Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks. IEEE Intelligent Vehicles Symposium 2017.
33	FusionNet			96.01 %	94.38 %	95.22 %	96.81 %	5.34 %	3.19 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
34	FuseNet			95.61 %	95.80 %	95.29 %	95.93 %	5.21 %	4.07 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
35	FCN_RGBD			95.55 %	95.46 %	95.67 %	95.44 %	4.75 %	4.56 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
36	Up-Conv-Poly		code	95.52 %	92.86 %	95.37 %	95.67 %	5.10 %	4.33 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.
37	DEEP-DIG			95.45 %	95.41 %	95.49 %	95.41 %	4.96 %	4.59 %	0.14 s	GPU @ 3.5 Ghz (Python + C/C++)
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.
38	ResNetPK			95.45 %	92.27 %	96.26 %	94.65 %	4.04 %	5.35 %	0.4s	GPU @ 1.5 Ghz (Python)
39	RDSN			95.32 %	91.01 %	94.87 %	95.76 %	5.69 %	4.24 %	0.25 s	GPU @ 2.5 Ghz (Python)
40	FCN-GCBs			95.29 %	91.27 %	95.16 %	95.43 %	5.33 %	4.57 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
41	SUNet			95.13 %	91.55 %	95.46 %	94.80 %	4.95 %	5.20 %	0.018s
42	CNN-FCRF			95.10 %	89.03 %	92.69 %	97.62 %	8.46 %	2.38 %	1 s	4 cores @ 3.5 Ghz (C/C++)
43	RSNetVGG			94.88 %	95.00 %	95.73 %	94.05 %	4.61 %	5.95 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
44	DFN			94.63 %	92.15 %	94.59 %	94.67 %	5.95 %	5.33 %	0.25 s	GPU @ >3.5 Ghz (Python)
45	fcn_rgbd			94.57 %	95.15 %	93.58 %	95.57 %	7.21 %	4.43 %	0.6 s	1 core @ 2.5 Ghz (C/C++)
46	HID-LS			94.48 %	91.28 %	95.17 %	93.80 %	5.24 %	6.20 %	0.25 s	4 cores @ 3.0 Ghz (C/C++)
47	DDN			94.17 %	92.70 %	96.73 %	91.74 %	3.41 %	8.26 %	2 s	GPU @ 2.5 Ghz (Python + C/C++)
R. Mohan: Deep Deconvolutional Networks for Scene Parsing. 2014.
48	FCN-LC			94.09 %	90.26 %	94.05 %	94.13 %	6.55 %	5.87 %	0.03 s	GPU Titan X
C. Mendes, V. Frémont and D. Wolf: Exploiting Fully Convolutional Neural Networks for Fast Road Detection. IEEE Conference on Robotics and Automation (ICRA) 2016.
49	Up-Conv			93.89 %	92.62 %	94.57 %	93.22 %	5.89 %	6.78 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.
50	HIM			93.55 %	90.38 %	94.18 %	92.92 %	6.31 %	7.08 %	7 s	>8 cores @ 2.5 Ghz (Python + C/C++)
D. Munoz, J. Bagnell and M. Hebert: Stacked Hierarchical Labeling. European Conference on Computer Vision (ECCV) 2010.
51	LiDAR-SPHnet			93.54 %	89.86 %	93.45 %	93.63 %	7.22 %	6.37 %	0.14 s	GPU @ 1.5 Ghz (Matlab)
52	LidarHisto		code	93.32 %	93.19 %	95.39 %	91.34 %	4.85 %	8.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
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.
53	StixelNet			93.26 %	87.15 %	90.63 %	96.06 %	10.92 %	3.94 %	1 s	GPU @ 2.5 Ghz (C/C++)
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.
54	Pos-ex		code	93.17 %	94.45 %	91.36 %	95.06 %	9.88 %	4.94 %	120 ms	GPU(K20) @ 0.7 Ghz (Matlab + Caffe)
55	HFM			93.12 %	87.10 %	90.58 %	95.82 %	10.96 %	4.18 %	5 s	2 cores @ 2.0 Ghz (C/C++)
56	ResAXN			92.99 %	94.76 %	93.75 %	92.24 %	6.76 %	7.76 %	0.06 s	GPU @ 1.5 Ghz (Python)
57	FTP			92.98 %	92.89 %	91.84 %	94.15 %	9.20 %	5.85 %	0.28 s	GPU @ 2.5 Ghz (C/C++)
A. Laddha, M. Kocamaz, L. Navarro-Serment and M. Hebert: Map-Supervised Road Detection. IEEE Intelligent Vehicles Symposium Proceedings 2016.
58	LWDS			92.81 %	94.66 %	94.41 %	91.25 %	5.93 %	8.75 %	0.07 s	GPU @ 2.5 Ghz (Python)
59	MixedCRF			92.75 %	90.24 %	94.03 %	91.50 %	6.39 %	8.50 %	6s	1 core @ 2.5 Ghz (Matlab + C/C++)
X. Han, H. Wang, J. Lu and C. Zhao: Road detection based on the fusion of Lidar and image data. 2017.
60	HybridCRF			91.95 %	86.44 %	94.01 %	89.98 %	6.30 %	10.02 %	1.5 s	1 core @ 2.5 Ghz (C/C++)
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.
61	CNN_LSTM			91.81 %	86.27 %	93.80 %	89.89 %	6.53 %	10.11 %	20 ms	GPU @ GTX950M (Python +Tensorflow)
62	PGM-ARS			91.76 %	84.80 %	88.05 %	95.80 %	14.30 %	4.20 %	0.05 s	i74700MQ @ 2.1Ghz (C/C++)
M. Passani, J. Yebes and L. Bergasa: Fast Pixelwise Road Inference Based on Uniformly Reweighted Belief Propagation . Proc. IEEE Intelligent Vehicles Symposium 2015.
63	TFSeg			91.41 %	93.00 %	91.68 %	91.15 %	9.09 %	8.85 %	0.07 s	GPU @ 1.0 Ghz (Python)
64	ProbBoost			91.36 %	84.92 %	88.18 %	94.78 %	13.97 %	5.22 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
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.
65	NNP			91.34 %	88.65 %	91.07 %	91.60 %	9.87 %	8.40 %	5 s	4 cores @ 2.5 Ghz (Matlab)
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.
66	FRS_SP			90.96 %	84.63 %	87.86 %	94.29 %	14.32 %	5.71 %	0.21 s	4 cores @ 3.0 Ghz (Matlab + C/C++)
67	SRF			90.77 %	92.44 %	89.35 %	92.23 %	12.08 %	7.77 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, B. Dai, D. Liu, D. Zhao and T. Wu: Monocular Road Detection Using Structured Random Forest. Int J Adv Robot Syst 2016.
68	RES3D-Velo			90.60 %	85.38 %	85.96 %	95.78 %	17.20 %	4.22 %	0.36 s	1 core @ 2.5 Ghz (C/C++)
P. Shinzato, D. Wolf and C. Stiller: Road Terrain Detection: Avoiding Common Obstacle Detection Assumptions Using Sensor Fusion. Intelligent Vehicles Symposium (IV) 2014.
69	CB			90.55 %	85.40 %	92.75 %	88.45 %	7.60 %	11.55 %	2 s	1 core @ 3.4 Ghz (Python) + GPU
C. Mendes, V. Frémont and D. Wolf: Vision-Based Road Detection using Contextual Blocks. 2015.
70	CNN+BiGRU			90.02 %	85.49 %	92.84 %	87.36 %	7.40 %	12.64 %	20 ms	GPU @ GTX950M (Python +Tensorflow)
71	MAP			89.97 %	92.14 %	87.47 %	92.62 %	14.58 %	7.38 %	0.28s	GPU
A. Laddha, M. Kocamaz, L. Navarro-Serment and M. Hebert: Map-Supervised Road Detection. IEEE Intelligent Vehicles Symposium Proceedings 2016.
72	DVFCN			89.95 %	93.93 %	89.74 %	90.17 %	11.33 %	9.83 %	0.07 s	GPU @ 2.5 Ghz (Python)
73	SPRAY			89.69 %	93.84 %	89.13 %	90.25 %	12.10 %	9.75 %	45 ms	NVIDIA GTX 580 (Python + OpenCL)
T. Kuehnl, F. Kummert and J. Fritsch: Spatial Ray Features for Real-Time Ego-Lane Extraction. Proc. IEEE Intelligent Transportation Systems 2012.
74	ARSL-AMI			89.56 %	82.82 %	85.87 %	93.59 %	16.93 %	6.41 %	0.05 s	4 cores @ 2.5 Ghz (C/C++)
M. Passani, J. Yebes and L. Bergasa: CRF-based semantic labeling in miniaturized road scenes . Proc. IEEE Intelligent Transportation Systems 2014.
75	FusedCRF			89.51 %	83.53 %	86.64 %	92.58 %	15.69 %	7.42 %	2 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, B. Dai, D. Liu, T. Hu and T. Wu: CRF based Road Detection with Multi-Sensor Fusion. Intelligent Vehicles Symposium (IV) 2015.
76	BM			89.41 %	80.61 %	83.43 %	96.30 %	21.02 %	3.70 %	2 s	2 cores @ 2.5 Ghz (Matlab)
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.
77	HistonBoost			88.73 %	81.57 %	84.49 %	93.42 %	18.85 %	6.58 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
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.
78	SegNet			88.59 %	83.54 %	88.35 %	88.84 %	12.88 %	11.16 %	0.01 s	8 cores @ 2.5 Ghz (C/C++)
79	geo+gpr+crf			88.20 %	82.33 %	85.32 %	91.27 %	17.26 %	8.73 %	30 s	1 core @ 2.0 Ghz (C/C++)
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.
80	GRES3D+VELO			88.19 %	88.65 %	83.98 %	92.85 %	19.48 %	7.15 %	60 ms	4 cores @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
81	SCRFFPFHGSP			87.96 %	83.16 %	90.01 %	86.01 %	10.50 %	13.99 %	5 s	8 cores @ 2.5 Ghz (C/C++, Matlab)
I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.
82	GRES3D+SELAS			87.57 %	90.52 %	85.92 %	89.28 %	16.08 %	10.72 %	110 ms	4 core @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
83	Strait			86.36 %	91.98 %	85.75 %	86.97 %	15.89 %	13.03 %	69 ms	GPU @ K20
84	CN			86.21 %	84.40 %	82.85 %	89.86 %	20.45 %	10.14 %	2 s	1 core @ 2.5 Ghz (C/C++)
J. Alvarez, T. Gevers, Y. LeCun and A. Lopez: Road Scene Segmentation from a Single Image. ECCV 2012 2012.
85	LWD			86.14 %	88.31 %	86.99 %	85.31 %	14.03 %	14.69 %	0.07 s	GPU @ 2.5 Ghz (Python)
86	SP-SS			85.07 %	79.86 %	85.97 %	84.20 %	15.11 %	15.80 %	0.01 s	4 cores @ 3.0 Ghz (Matlab + C/C++)
87	CNN_LSTM			84.98 %	83.43 %	90.34 %	80.22 %	9.43 %	19.78 %	54 ms	GPU @ 2.0 Ghz (Python + C/C++)
88	RES3D-Stereo			83.62 %	85.74 %	79.81 %	87.81 %	24.42 %	12.19 %	0.7 s	1 core @ 2.5 Ghz (C/C++)
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.
89	SPlane			82.28 %	82.83 %	76.85 %	88.53 %	29.32 %	11.47 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
90	SPlane + BL			82.04 %	85.56 %	75.11 %	90.39 %	32.93 %	9.61 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
91	ANN			80.95 %	68.36 %	69.95 %	96.05 %	45.35 %	3.95 %	3 s	1 core @ 3.0 Ghz (C/C++)
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.
92	LKW			75.48 %	78.73 %	65.97 %	88.18 %	50.00 %	11.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
93	VAP			71.83 %	60.64 %	62.48 %	84.48 %	55.76 %	15.52 %	1 s	1 core @ 2.5 Ghz (Matlab)
ERROR: Wrong syntax in BIBTEX file.

Table as LaTeX | Only published Methods

UU_ROAD

	Method	Setting	Code	MaxF	AP	PRE	REC	FPR	FNR	Runtime	Environment
1	iDST-VT			96.60 %	93.01 %	96.03 %	97.17 %	1.31 %	2.83 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
2	VGGFCN-6D			96.23 %	92.18 %	95.77 %	96.70 %	1.39 %	3.30 %	.006 s	GPU @ 3.5 Ghz (Python)
3	UNV			95.71 %	90.32 %	95.13 %	96.30 %	1.61 %	3.70 %	1.2 s	GPU @ 3.0 Ghz (Python + C/C++)
4	DFFA			95.57 %	89.21 %	95.67 %	95.47 %	1.41 %	4.53 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
5	KRS			95.55 %	92.90 %	95.81 %	95.30 %	1.36 %	4.70 %	0.3 s	GPU @ 2.5 Ghz (Python)
6	KRSF			95.53 %	92.96 %	96.25 %	94.83 %	1.21 %	5.17 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
7	NF2CNN			95.47 %	86.98 %	93.22 %	97.84 %	2.32 %	2.16 %	.006 s	GPU @ 3.5 Ghz (Python)
8	YhY		code	95.39 %	88.50 %	94.89 %	95.90 %	1.68 %	4.10 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
9	WNet			95.23 %	91.93 %	94.51 %	95.96 %	1.82 %	4.04 %	0.1 s	4 cores @ 2.5 Ghz (Python)
10	BIRD			94.89 %	92.27 %	94.98 %	94.80 %	1.63 %	5.20 %	25 ms	GPU @ 2.5 Ghz (Python + C/C++)
11	RSNet			94.84 %	91.65 %	94.56 %	95.13 %	1.78 %	4.87 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
12	KRS			94.60 %	92.83 %	94.96 %	94.25 %	1.63 %	5.75 %	1 s	GPU @ 2.5 Ghz (Python)
13	LidCamNet			94.54 %	92.74 %	94.64 %	94.45 %	1.74 %	5.55 %	0.15 s	GPU @ 2.5 Ghz (Python)
14	WSLGAN			94.54 %	87.70 %	94.01 %	95.09 %	1.97 %	4.91 %	800ms	GPU @ 1.5 Ghz (Python)
15	SSLGAN			94.40 %	87.84 %	94.17 %	94.63 %	1.91 %	5.37 %	700ms	GPU @ 1.5 Ghz (Python)
16	baseline			94.14 %	92.15 %	93.69 %	94.60 %	2.08 %	5.40 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
17	MVnet			94.07 %	89.03 %	95.48 %	92.71 %	1.43 %	7.29 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
18	RSNet2			93.98 %	91.88 %	93.81 %	94.15 %	2.03 %	5.85 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
19	RSNet-			93.90 %	91.69 %	92.94 %	94.88 %	2.35 %	5.12 %	0.07 s	GPU @ 2.5 Ghz (Python)
20	SPN			93.89 %	88.19 %	94.56 %	93.23 %	1.75 %	6.77 %	1 s	1 core @ 2.5 Ghz (C/C++)
21	MMN			93.87 %	91.23 %	93.26 %	94.48 %	2.22 %	5.52 %	0.1 s	GPU @ 2.5 Ghz (C/C++)
22	CoDNN			93.81 %	92.41 %	94.20 %	93.43 %	1.88 %	6.57 %	0.01 s	1 core @ 2.5 Ghz (Python + C/C++)
23	FNETMS			93.71 %	91.79 %	94.08 %	93.34 %	1.91 %	6.66 %	0.04 s	GPU @ 2.0 Ghz (Python + C/C++)
24	MultiNet		code	93.69 %	92.55 %	94.24 %	93.14 %	1.85 %	6.86 %	0.17 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Teichmann, M. Weber, J. Zoellner, R. Cipolla and R. Urtasun: MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving. CoRR 2016.
25	IDA-Fusion			93.63 %	86.07 %	92.22 %	95.08 %	2.61 %	4.92 %	0.1 s	4 cores @ 3.5 Ghz (C/C++)
26	wt			93.58 %	92.14 %	93.34 %	93.83 %	2.18 %	6.17 %	0.1 s	GPU @ 1.0 Ghz (Python)
27	StixelNet II			93.40 %	85.01 %	91.05 %	95.87 %	3.07 %	4.13 %	1.2 s	1 core @ 3.0 Ghz (Matlab + C/C++)
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.
28	RBNet			93.21 %	89.18 %	92.81 %	93.60 %	2.36 %	6.40 %	0.18 s	GPU @ 2.5 Ghz (Matlab + C/C++)
Z. Chen and Z. Chen: RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection. International Conference on Neural Information Processing 2017.
29	FDN			93.10 %	92.24 %	93.44 %	92.77 %	2.12 %	7.23 %	0.2 s	GPU 1 core @ 2.5 Ghz (Python)
30	FuseNet			92.97 %	92.47 %	93.44 %	92.51 %	2.12 %	7.49 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
31	FusionNet			92.89 %	90.69 %	92.75 %	93.03 %	2.37 %	6.97 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
32	MBN			92.89 %	83.53 %	89.43 %	96.63 %	3.72 %	3.37 %	0.16 s	GPU @ 2.5 Ghz (Python)
33	Up-Conv-Poly		code	92.65 %	89.20 %	92.85 %	92.45 %	2.32 %	7.55 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.
34	ResNetPK			92.56 %	86.93 %	93.16 %	91.96 %	2.20 %	8.04 %	0.4s	GPU @ 1.5 Ghz (Python)
35	TDCac1 CNN			92.30 %	86.21 %	92.37 %	92.23 %	2.48 %	7.77 %	.093 s	1 core @ 1.0 Ghz (C/C++)
36	LoDNN			92.29 %	90.35 %	90.81 %	93.81 %	3.09 %	6.19 %	18 ms	GPU @ 2.5 Ghz (Torch)
L. Caltagirone, S. Scheidegger, L. Svensson and M. Wahde: Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks. IEEE Intelligent Vehicles Symposium 2017.
37	FCN-GCBs			92.10 %	83.69 %	89.61 %	94.73 %	3.58 %	5.27 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
38	FCN_RGBD			91.99 %	92.09 %	92.84 %	91.16 %	2.29 %	8.84 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
39	RDSN			91.98 %	85.64 %	91.45 %	92.53 %	2.82 %	7.47 %	0.25 s	GPU @ 2.5 Ghz (Python)
40	Up-Conv			91.89 %	89.44 %	92.59 %	91.20 %	2.38 %	8.80 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.
41	DDN			91.76 %	86.84 %	93.06 %	90.50 %	2.20 %	9.50 %	2 s	GPU @ 2.5 Ghz (Python + C/C++)
R. Mohan: Deep Deconvolutional Networks for Scene Parsing. 2014.
42	RSNetVGG			91.72 %	91.52 %	92.62 %	90.84 %	2.36 %	9.16 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
43	DFN			91.70 %	87.72 %	91.51 %	91.89 %	2.78 %	8.11 %	0.25 s	GPU @ >3.5 Ghz (Python)
44	DEEP-DIG			91.27 %	91.77 %	91.32 %	91.22 %	2.82 %	8.78 %	0.14 s	GPU @ 3.5 Ghz (Python + C/C++)
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.
45	SUNet			91.10 %	81.62 %	87.32 %	95.22 %	4.50 %	4.78 %	0.018s
46	RD			90.03 %	78.89 %	90.96 %	89.11 %	2.89 %	10.89 %	4 s	1 core @ 3.0 Ghz (Matlab + C/C++)
47	FTP			89.62 %	88.93 %	89.10 %	90.14 %	3.59 %	9.86 %	0.28 s	GPU @ 2.5 Ghz (C/C++)
A. Laddha, M. Kocamaz, L. Navarro-Serment and M. Hebert: Map-Supervised Road Detection. IEEE Intelligent Vehicles Symposium Proceedings 2016.
48	HID-LS			89.37 %	81.20 %	86.86 %	92.02 %	4.53 %	7.98 %	0.25 s	4 cores @ 3.0 Ghz (C/C++)
49	HFM			89.20 %	80.48 %	86.07 %	92.56 %	4.88 %	7.44 %	5 s	2 cores @ 2.0 Ghz (C/C++)
50	fcn_rgbd			89.16 %	91.48 %	90.38 %	87.97 %	3.05 %	12.03 %	0.6 s	1 core @ 2.5 Ghz (C/C++)
51	LiDAR-SPHnet			88.98 %	80.48 %	86.07 %	92.09 %	4.86 %	7.91 %	0.14 s	GPU @ 1.5 Ghz (Matlab)
52	HybridCRF			88.53 %	80.79 %	86.41 %	90.76 %	4.65 %	9.24 %	1.5 s	1 core @ 2.5 Ghz (C/C++)
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.
53	TFSeg			87.42 %	88.58 %	87.28 %	87.56 %	4.16 %	12.44 %	0.07 s	GPU @ 1.0 Ghz (Python)
54	IINM			87.16 %	78.96 %	84.40 %	90.10 %	5.43 %	9.90 %	0.11 s	4 cores @ 3.0 Ghz (C/C++)
55	LidarHisto		code	86.55 %	81.13 %	90.71 %	82.75 %	2.76 %	17.25 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
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.
56	CNN-FCRF			86.33 %	72.39 %	83.02 %	89.92 %	5.99 %	10.08 %	1 s	4 cores @ 3.5 Ghz (C/C++)
57	FCN-LC			86.27 %	75.37 %	86.65 %	85.89 %	4.31 %	14.11 %	0.03 s	GPU Titan X
C. Mendes, V. Frémont and D. Wolf: Exploiting Fully Convolutional Neural Networks for Fast Road Detection. IEEE Conference on Robotics and Automation (ICRA) 2016.
58	CB			86.13 %	75.21 %	86.47 %	85.80 %	4.38 %	14.20 %	2 s	1 core @ 3.4 Ghz (Python) + GPU
C. Mendes, V. Frémont and D. Wolf: Vision-Based Road Detection using Contextual Blocks. 2015.
59	StixelNet			86.06 %	72.05 %	82.61 %	89.82 %	6.16 %	10.18 %	1 s	GPU @ 2.5 Ghz (C/C++)
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.
60	HIM			85.76 %	76.18 %	87.65 %	83.95 %	3.86 %	16.05 %	7 s	>8 cores @ 2.5 Ghz (Python + C/C++)
D. Munoz, J. Bagnell and M. Hebert: Stacked Hierarchical Labeling. European Conference on Computer Vision (ECCV) 2010.
61	MixedCRF			85.69 %	75.12 %	80.17 %	92.02 %	7.42 %	7.98 %	6s	1 core @ 2.5 Ghz (Matlab + C/C++)
X. Han, H. Wang, J. Lu and C. Zhao: Road detection based on the fusion of Lidar and image data. 2017.
62	NNP			85.55 %	76.90 %	85.36 %	85.75 %	4.79 %	14.25 %	5 s	4 cores @ 2.5 Ghz (Matlab)
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.
63	DVFCN			85.37 %	89.13 %	85.44 %	85.30 %	4.74 %	14.70 %	0.07 s	GPU @ 2.5 Ghz (Python)
64	CNN_LSTM			85.15 %	76.59 %	88.15 %	82.36 %	3.61 %	17.64 %	20 ms	GPU @ GTX950M (Python +Tensorflow)
65	LWD			84.96 %	87.51 %	82.18 %	87.93 %	6.21 %	12.07 %	0.07 s	GPU @ 2.5 Ghz (Python)
66	LWDS			84.50 %	83.24 %	87.46 %	81.72 %	3.82 %	18.28 %	0.07 s	GPU @ 2.5 Ghz (Python)
67	FusedCRF			84.49 %	72.35 %	77.13 %	93.40 %	9.02 %	6.60 %	2 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, B. Dai, D. Liu, T. Hu and T. Wu: CRF based Road Detection with Multi-Sensor Fusion. Intelligent Vehicles Symposium (IV) 2015.
68	MAP			84.44 %	87.17 %	83.66 %	85.23 %	5.42 %	14.77 %	0.28s	GPU
A. Laddha, M. Kocamaz, L. Navarro-Serment and M. Hebert: Map-Supervised Road Detection. IEEE Intelligent Vehicles Symposium Proceedings 2016.
69	Pos-ex		code	84.33 %	87.33 %	82.12 %	86.66 %	6.15 %	13.34 %	120 ms	GPU(K20) @ 0.7 Ghz (Matlab + Caffe)
70	GRES3D+VELO			84.14 %	80.20 %	80.57 %	88.03 %	6.92 %	11.97 %	60 ms	4 cores @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
71	ResAXN			83.68 %	87.44 %	84.04 %	83.33 %	5.16 %	16.67 %	0.06 s	GPU @ 1.5 Ghz (Python)
72	RES3D-Velo			83.63 %	72.58 %	77.38 %	90.97 %	8.67 %	9.03 %	0.36 s	1 core @ 2.5 Ghz (C/C++)
P. Shinzato, D. Wolf and C. Stiller: Road Terrain Detection: Avoiding Common Obstacle Detection Assumptions Using Sensor Fusion. Intelligent Vehicles Symposium (IV) 2014.
73	CNN+BiGRU			82.91 %	71.28 %	88.79 %	77.76 %	3.20 %	22.24 %	20 ms	GPU @ GTX950M (Python +Tensorflow)
74	SPRAY			82.71 %	87.19 %	82.16 %	83.26 %	5.89 %	16.74 %	45 ms	NVIDIA GTX 580 (Python + OpenCL)
T. Kuehnl, F. Kummert and J. Fritsch: Spatial Ray Features for Real-Time Ego-Lane Extraction. Proc. IEEE Intelligent Transportation Systems 2012.
75	GRES3D+SELAS			82.70 %	83.95 %	78.54 %	87.32 %	7.77 %	12.68 %	110 ms	4 core @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
76	geo+gpr+crf			81.00 %	69.74 %	79.78 %	82.27 %	6.79 %	17.73 %	30 s	1 core @ 2.0 Ghz (C/C++)
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.
77	SCRFFPFHGSP			80.78 %	70.80 %	81.07 %	80.50 %	6.13 %	19.50 %	5 s	8 cores @ 2.5 Ghz (C/C++, Matlab)
I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.
78	ProbBoost			80.76 %	68.70 %	85.25 %	76.72 %	4.33 %	23.28 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
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.
79	FRS_SP			80.02 %	67.93 %	77.56 %	82.64 %	7.79 %	17.36 %	0.21 s	4 cores @ 3.0 Ghz (Matlab + C/C++)
80	PGM-ARS			79.94 %	67.77 %	77.37 %	82.67 %	7.88 %	17.33 %	0.05 s	i74700MQ @ 2.1Ghz (C/C++)
M. Passani, J. Yebes and L. Bergasa: Fast Pixelwise Road Inference Based on Uniformly Reweighted Belief Propagation . Proc. IEEE Intelligent Vehicles Symposium 2015.
81	Strait			79.75 %	84.94 %	78.52 %	81.02 %	7.22 %	18.98 %	69 ms	GPU @ K20
82	RES3D-Stereo			78.75 %	73.60 %	77.63 %	79.90 %	7.50 %	20.10 %	0.7 s	1 core @ 2.5 Ghz (C/C++)
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.
83	SP-SS			78.47 %	65.18 %	74.20 %	83.25 %	9.43 %	16.75 %	0.01 s	4 cores @ 3.0 Ghz (Matlab + C/C++)
84	BM			78.43 %	62.46 %	70.87 %	87.80 %	11.76 %	12.20 %	2 s	2 cores @ 2.5 Ghz (Matlab)
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.
85	SegNet			77.23 %	69.23 %	82.29 %	72.76 %	5.10 %	27.24 %	0.01 s	8 cores @ 2.5 Ghz (C/C++)
86	CNN_LSTM			76.28 %	65.25 %	80.51 %	72.47 %	5.72 %	27.53 %	54 ms	GPU @ 2.0 Ghz (Python + C/C++)
87	SRF			76.07 %	79.97 %	71.47 %	81.31 %	10.57 %	18.69 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, B. Dai, D. Liu, D. Zhao and T. Wu: Monocular Road Detection Using Structured Random Forest. Int J Adv Robot Syst 2016.
88	HistonBoost			74.19 %	63.01 %	77.43 %	71.22 %	6.77 %	28.78 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
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.
89	SPlane + BL			74.02 %	79.61 %	65.15 %	85.68 %	14.93 %	14.32 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
90	SPlane			73.30 %	69.11 %	65.39 %	83.38 %	14.38 %	16.62 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
91	CN			72.25 %	66.61 %	71.96 %	72.54 %	9.21 %	27.46 %	2 s	1 core @ 2.5 Ghz (C/C++)
J. Alvarez, T. Gevers, Y. LeCun and A. Lopez: Road Scene Segmentation from a Single Image. ECCV 2012 2012.
92	ARSL-AMI			70.33 %	61.97 %	83.33 %	60.84 %	3.97 %	39.16 %	0.05 s	4 cores @ 2.5 Ghz (C/C++)
M. Passani, J. Yebes and L. Bergasa: CRF-based semantic labeling in miniaturized road scenes . Proc. IEEE Intelligent Transportation Systems 2014.
93	LKW			69.65 %	74.02 %	65.45 %	74.42 %	12.80 %	25.58 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
94	VAP			54.62 %	37.65 %	38.96 %	91.32 %	46.62 %	8.68 %	1 s	1 core @ 2.5 Ghz (Matlab)
ERROR: Wrong syntax in BIBTEX file.
95	ANN			54.07 %	36.61 %	39.28 %	86.69 %	43.67 %	13.31 %	3 s	1 core @ 3.0 Ghz (C/C++)
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.

Table as LaTeX | Only published Methods

URBAN_ROAD

	Method	Setting	Code	MaxF	AP	PRE	REC	FPR	FNR	Runtime	Environment
1	iDST-VT			97.19 %	94.05 %	97.09 %	97.29 %	1.61 %	2.71 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
2	VGGFCN-6D			97.05 %	93.50 %	96.85 %	97.26 %	1.75 %	2.74 %	.006 s	GPU @ 3.5 Ghz (Python)
3	UNV			96.74 %	92.39 %	96.88 %	96.60 %	1.71 %	3.40 %	1.2 s	GPU @ 3.0 Ghz (Python + C/C++)
4	NF2CNN			96.70 %	89.93 %	95.37 %	98.07 %	2.62 %	1.93 %	.006 s	GPU @ 3.5 Ghz (Python)
5	KRSF			96.50 %	94.01 %	96.74 %	96.27 %	1.78 %	3.73 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
6	YhY		code	96.44 %	90.43 %	95.92 %	96.97 %	2.27 %	3.03 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
7	KRS			96.41 %	93.96 %	96.43 %	96.38 %	1.96 %	3.62 %	0.3 s	GPU @ 2.5 Ghz (Python)
8	DFFA			96.35 %	90.52 %	96.02 %	96.69 %	2.21 %	3.31 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
9	LidCamNet			96.03 %	93.93 %	96.23 %	95.83 %	2.07 %	4.17 %	0.15 s	GPU @ 2.5 Ghz (Python)
10	BIRD			95.93 %	93.32 %	95.52 %	96.35 %	2.49 %	3.65 %	25 ms	GPU @ 2.5 Ghz (Python + C/C++)
11	RSNet			95.86 %	93.21 %	95.68 %	96.05 %	2.39 %	3.95 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
12	MVnet			95.83 %	91.67 %	97.29 %	94.41 %	1.45 %	5.59 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
13	WNet			95.82 %	93.31 %	95.88 %	95.76 %	2.26 %	4.24 %	0.1 s	4 cores @ 2.5 Ghz (Python)
14	WSLGAN			95.70 %	90.17 %	95.64 %	95.77 %	2.40 %	4.23 %	800ms	GPU @ 1.5 Ghz (Python)
15	KRS			95.64 %	93.89 %	95.79 %	95.48 %	2.31 %	4.52 %	1 s	GPU @ 2.5 Ghz (Python)
16	baseline			95.58 %	93.51 %	95.19 %	95.97 %	2.67 %	4.03 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
17	SSLGAN			95.53 %	90.35 %	95.84 %	95.24 %	2.28 %	4.76 %	700ms	GPU @ 1.5 Ghz (Python)
18	RSNet2			95.35 %	93.32 %	95.20 %	95.49 %	2.65 %	4.51 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
19	RSNet-			95.29 %	93.37 %	94.96 %	95.62 %	2.80 %	4.38 %	0.07 s	GPU @ 2.5 Ghz (Python)
20	SPN			95.29 %	90.44 %	95.93 %	94.65 %	2.21 %	5.35 %	1 s	1 core @ 2.5 Ghz (C/C++)
21	IDA-Fusion			95.22 %	89.31 %	94.69 %	95.76 %	2.96 %	4.24 %	0.1 s	4 cores @ 3.5 Ghz (C/C++)
22	MMN			95.12 %	92.99 %	94.82 %	95.42 %	2.87 %	4.58 %	0.1 s	GPU @ 2.5 Ghz (C/C++)
23	CoDNN			95.06 %	93.58 %	95.24 %	94.88 %	2.61 %	5.12 %	0.01 s	1 core @ 2.5 Ghz (Python + C/C++)
24	FNETMS			94.99 %	93.18 %	94.90 %	95.09 %	2.82 %	4.91 %	0.04 s	GPU @ 2.0 Ghz (Python + C/C++)
25	RBNet			94.97 %	91.49 %	94.94 %	95.01 %	2.79 %	4.99 %	0.18 s	GPU @ 2.5 Ghz (Matlab + C/C++)
Z. Chen and Z. Chen: RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection. International Conference on Neural Information Processing 2017.
26	StixelNet II			94.88 %	87.75 %	92.97 %	96.87 %	4.04 %	3.13 %	1.2 s	1 core @ 3.0 Ghz (Matlab + C/C++)
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.
27	MultiNet		code	94.88 %	93.71 %	94.84 %	94.91 %	2.85 %	5.09 %	0.17 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Teichmann, M. Weber, J. Zoellner, R. Cipolla and R. Urtasun: MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving. CoRR 2016.
28	TDCac1 CNN			94.81 %	89.82 %	95.25 %	94.38 %	2.59 %	5.62 %	.093 s	1 core @ 1.0 Ghz (C/C++)
29	FDN			94.75 %	93.63 %	94.65 %	94.86 %	2.96 %	5.14 %	0.2 s	GPU 1 core @ 2.5 Ghz (Python)
30	FusionNet			94.67 %	92.54 %	94.73 %	94.61 %	2.90 %	5.39 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
31	wt			94.63 %	93.56 %	94.38 %	94.88 %	3.11 %	5.12 %	0.1 s	GPU @ 1.0 Ghz (Python)
32	MBN			94.63 %	87.37 %	92.55 %	96.80 %	4.29 %	3.20 %	0.16 s	GPU @ 2.5 Ghz (Python)
33	FuseNet			94.41 %	93.77 %	94.70 %	94.11 %	2.90 %	5.89 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
34	ResNetPK			94.25 %	89.66 %	95.07 %	93.45 %	2.67 %	6.55 %	0.4s	GPU @ 1.5 Ghz (Python)
35	FCN_RGBD			94.14 %	93.74 %	94.81 %	93.48 %	2.82 %	6.52 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
36	FCN-GCBs			94.08 %	87.66 %	92.87 %	95.32 %	4.03 %	4.68 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
37	LoDNN			94.07 %	92.03 %	92.81 %	95.37 %	4.07 %	4.63 %	18 ms	GPU @ 2.5 Ghz (Torch)
L. Caltagirone, S. Scheidegger, L. Svensson and M. Wahde: Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks. IEEE Intelligent Vehicles Symposium 2017.
38	DEEP-DIG			93.98 %	93.65 %	94.26 %	93.69 %	3.14 %	6.31 %	0.14 s	GPU @ 3.5 Ghz (Python + C/C++)
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.
39	Up-Conv-Poly		code	93.83 %	90.47 %	94.00 %	93.67 %	3.29 %	6.33 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.
40	RDSN			93.75 %	88.33 %	93.55 %	93.96 %	3.57 %	6.04 %	0.25 s	GPU @ 2.5 Ghz (Python)
41	DDN			93.43 %	89.67 %	95.09 %	91.82 %	2.61 %	8.18 %	2 s	GPU @ 2.5 Ghz (Python + C/C++)
R. Mohan: Deep Deconvolutional Networks for Scene Parsing. 2014.
42	SUNet			93.38 %	87.18 %	92.35 %	94.44 %	4.31 %	5.56 %	0.018s
43	RSNetVGG			93.24 %	93.10 %	94.68 %	91.84 %	2.85 %	8.16 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
44	DFN			93.23 %	89.59 %	93.11 %	93.35 %	3.81 %	6.65 %	0.25 s	GPU @ >3.5 Ghz (Python)
45	HID-LS			92.53 %	86.25 %	91.32 %	93.76 %	4.91 %	6.24 %	0.25 s	4 cores @ 3.0 Ghz (C/C++)
46	Up-Conv			92.39 %	90.24 %	93.03 %	91.76 %	3.79 %	8.24 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.
47	fcn_rgbd			92.25 %	93.15 %	91.62 %	92.88 %	4.68 %	7.12 %	0.6 s	1 core @ 2.5 Ghz (C/C++)
48	LiDAR-SPHnet			91.79 %	84.76 %	89.68 %	94.00 %	5.96 %	6.00 %	0.14 s	GPU @ 1.5 Ghz (Matlab)
49	FTP			91.61 %	90.96 %	91.04 %	92.20 %	5.00 %	7.80 %	0.28 s	GPU @ 2.5 Ghz (C/C++)
A. Laddha, M. Kocamaz, L. Navarro-Serment and M. Hebert: Map-Supervised Road Detection. IEEE Intelligent Vehicles Symposium Proceedings 2016.
50	CNN-FCRF			91.40 %	84.22 %	89.09 %	93.84 %	6.33 %	6.16 %	1 s	4 cores @ 3.5 Ghz (C/C++)
51	HFM			90.88 %	83.10 %	87.86 %	94.12 %	7.16 %	5.88 %	5 s	2 cores @ 2.0 Ghz (C/C++)
52	HybridCRF			90.81 %	86.01 %	91.05 %	90.57 %	4.90 %	9.43 %	1.5 s	1 core @ 2.5 Ghz (C/C++)
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.
53	FCN-LC			90.79 %	85.83 %	90.87 %	90.72 %	5.02 %	9.28 %	0.03 s	GPU Titan X
C. Mendes, V. Frémont and D. Wolf: Exploiting Fully Convolutional Neural Networks for Fast Road Detection. IEEE Conference on Robotics and Automation (ICRA) 2016.
54	LidarHisto		code	90.67 %	84.79 %	93.06 %	88.41 %	3.63 %	11.59 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
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.
55	HIM			90.64 %	81.42 %	91.62 %	89.68 %	4.52 %	10.32 %	7 s	>8 cores @ 2.5 Ghz (Python + C/C++)
D. Munoz, J. Bagnell and M. Hebert: Stacked Hierarchical Labeling. European Conference on Computer Vision (ECCV) 2010.
56	MixedCRF			90.59 %	84.24 %	89.11 %	92.13 %	6.20 %	7.87 %	6s	1 core @ 2.5 Ghz (Matlab + C/C++)
X. Han, H. Wang, J. Lu and C. Zhao: Road detection based on the fusion of Lidar and image data. 2017.
57	LWDS			89.83 %	87.04 %	91.61 %	88.12 %	4.45 %	11.88 %	0.07 s	GPU @ 2.5 Ghz (Python)
58	NNP			89.68 %	86.50 %	89.67 %	89.68 %	5.69 %	10.32 %	5 s	4 cores @ 2.5 Ghz (Matlab)
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.
59	TFSeg			89.65 %	89.24 %	88.79 %	90.52 %	6.30 %	9.48 %	0.07 s	GPU @ 1.0 Ghz (Python)
60	ResAXN			89.39 %	91.91 %	90.84 %	87.98 %	4.89 %	12.02 %	0.06 s	GPU @ 1.5 Ghz (Python)
61	StixelNet			89.12 %	81.23 %	85.80 %	92.71 %	8.45 %	7.29 %	1 s	GPU @ 2.5 Ghz (C/C++)
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.
62	Pos-ex		code	89.08 %	91.60 %	88.12 %	90.06 %	6.69 %	9.94 %	120 ms	GPU(K20) @ 0.7 Ghz (Matlab + Caffe)
63	CB			88.97 %	79.69 %	89.50 %	88.44 %	5.71 %	11.56 %	2 s	1 core @ 3.4 Ghz (Python) + GPU
C. Mendes, V. Frémont and D. Wolf: Vision-Based Road Detection using Contextual Blocks. 2015.
64	DVFCN			88.34 %	91.70 %	88.51 %	88.17 %	6.30 %	11.83 %	0.07 s	GPU @ 2.5 Ghz (Python)
65	FusedCRF			88.25 %	79.24 %	83.62 %	93.44 %	10.08 %	6.56 %	2 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, B. Dai, D. Liu, T. Hu and T. Wu: CRF based Road Detection with Multi-Sensor Fusion. Intelligent Vehicles Symposium (IV) 2015.
66	CNN_LSTM			88.23 %	81.09 %	91.22 %	85.43 %	4.53 %	14.57 %	20 ms	GPU @ GTX950M (Python +Tensorflow)
67	MAP			87.80 %	89.96 %	86.01 %	89.66 %	8.04 %	10.34 %	0.28s	GPU
A. Laddha, M. Kocamaz, L. Navarro-Serment and M. Hebert: Map-Supervised Road Detection. IEEE Intelligent Vehicles Symposium Proceedings 2016.
68	ProbBoost			87.78 %	77.30 %	86.59 %	89.01 %	7.60 %	10.99 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
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.
69	SPRAY			87.09 %	91.12 %	87.10 %	87.08 %	7.10 %	12.92 %	45 ms	NVIDIA GTX 580 (Python + OpenCL)
T. Kuehnl, F. Kummert and J. Fritsch: Spatial Ray Features for Real-Time Ego-Lane Extraction. Proc. IEEE Intelligent Transportation Systems 2012.
70	CNN+BiGRU			86.91 %	81.11 %	91.24 %	82.97 %	4.39 %	17.03 %	20 ms	GPU @ GTX950M (Python +Tensorflow)
71	RES3D-Velo			86.58 %	78.34 %	82.63 %	90.92 %	10.53 %	9.08 %	0.36 s	1 core @ 2.5 Ghz (C/C++)
P. Shinzato, D. Wolf and C. Stiller: Road Terrain Detection: Avoiding Common Obstacle Detection Assumptions Using Sensor Fusion. Intelligent Vehicles Symposium (IV) 2014.
72	GRES3D+VELO			86.07 %	84.34 %	82.16 %	90.38 %	10.81 %	9.62 %	60 ms	4 cores @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
73	FRS_SP			85.97 %	77.81 %	82.04 %	90.31 %	10.89 %	9.69 %	0.21 s	4 cores @ 3.0 Ghz (Matlab + C/C++)
74	LWD			85.74 %	84.86 %	83.76 %	87.81 %	9.38 %	12.19 %	0.07 s	GPU @ 2.5 Ghz (Python)
75	PGM-ARS			85.69 %	73.83 %	82.34 %	89.33 %	10.56 %	10.67 %	0.05 s	i74700MQ @ 2.1Ghz (C/C++)
M. Passani, J. Yebes and L. Bergasa: Fast Pixelwise Road Inference Based on Uniformly Reweighted Belief Propagation . Proc. IEEE Intelligent Vehicles Symposium 2015.
76	geo+gpr+crf			85.56 %	74.21 %	82.81 %	88.50 %	10.12 %	11.50 %	30 s	1 core @ 2.0 Ghz (C/C++)
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.
77	GRES3D+SELAS			85.09 %	86.86 %	82.27 %	88.10 %	10.46 %	11.90 %	110 ms	4 core @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
78	SCRFFPFHGSP			84.93 %	76.31 %	85.37 %	84.49 %	7.98 %	15.51 %	5 s	8 cores @ 2.5 Ghz (C/C++, Matlab)
I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.
79	SegNet			84.04 %	78.76 %	85.50 %	82.63 %	7.72 %	17.37 %	0.01 s	8 cores @ 2.5 Ghz (C/C++)
80	Strait			84.03 %	88.71 %	82.95 %	85.13 %	9.64 %	14.87 %	69 ms	GPU @ K20
81	HistonBoost			83.92 %	73.75 %	82.24 %	85.66 %	10.19 %	14.34 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
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.
82	BM			83.47 %	72.23 %	75.90 %	92.72 %	16.22 %	7.28 %	2 s	2 cores @ 2.5 Ghz (Matlab)
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.
83	SRF			82.44 %	87.37 %	80.60 %	84.36 %	11.18 %	15.64 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, B. Dai, D. Liu, D. Zhao and T. Wu: Monocular Road Detection Using Structured Random Forest. Int J Adv Robot Syst 2016.
84	SP-SS			82.36 %	72.31 %	80.48 %	84.33 %	11.27 %	15.67 %	0.01 s	4 cores @ 3.0 Ghz (Matlab + C/C++)
85	RES3D-Stereo			81.08 %	81.68 %	78.14 %	84.24 %	12.98 %	15.76 %	0.7 s	1 core @ 2.5 Ghz (C/C++)
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.
86	CNN_LSTM			80.91 %	72.11 %	85.84 %	76.52 %	6.96 %	23.48 %	54 ms	GPU @ 2.0 Ghz (Python + C/C++)
87	ARSL-AMI			80.36 %	70.23 %	83.24 %	77.67 %	8.61 %	22.33 %	0.05 s	4 cores @ 2.5 Ghz (C/C++)
M. Passani, J. Yebes and L. Bergasa: CRF-based semantic labeling in miniaturized road scenes . Proc. IEEE Intelligent Transportation Systems 2014.
88	SPlane + BL			79.63 %	83.90 %	72.59 %	88.17 %	18.34 %	11.83 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
89	CN			79.02 %	78.80 %	76.64 %	81.55 %	13.69 %	18.45 %	2 s	1 core @ 2.5 Ghz (C/C++)
J. Alvarez, T. Gevers, Y. LeCun and A. Lopez: Road Scene Segmentation from a Single Image. ECCV 2012 2012.
90	SPlane			78.69 %	77.16 %	71.96 %	86.80 %	18.63 %	13.20 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
91	LKW			75.53 %	79.80 %	69.68 %	82.44 %	19.75 %	17.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
92	ANN			67.70 %	52.50 %	54.19 %	90.17 %	41.98 %	9.83 %	3 s	1 core @ 3.0 Ghz (C/C++)
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.
93	VAP			62.78 %	46.54 %	48.99 %	87.41 %	50.14 %	12.59 %	1 s	1 core @ 2.5 Ghz (Matlab)
ERROR: Wrong syntax in BIBTEX file.

Table as LaTeX | Only published Methods

UM_LANE

	Method	Setting	Code	MaxF	AP	PRE	REC	FPR	FNR	Runtime	Environment
1	DFFA			94.31 %	88.03 %	95.33 %	93.31 %	0.80 %	6.69 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
2	AILabsLane			93.72 %	87.32 %	93.57 %	93.88 %	1.14 %	6.12 %	0.25 s	GPU @ 2.5 Ghz (C/C++)
3	BIRD			92.70 %	90.94 %	92.39 %	93.01 %	1.35 %	6.99 %	25 ms	GPU @ 2.5 Ghz (Python + C/C++)
4	NVLaneNet			91.86 %	91.42 %	90.89 %	92.85 %	1.64 %	7.15 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
5	FCN-GCBs			91.24 %	85.14 %	92.15 %	90.35 %	1.35 %	9.65 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
6	RBNet			90.54 %	82.03 %	94.92 %	86.56 %	0.82 %	13.44 %	0.18 s	GPU @ 2.5 Ghz (Matlab + C/C++)
Z. Chen and Z. Chen: RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection. International Conference on Neural Information Processing 2017.
7	RD			90.01 %	81.60 %	88.26 %	91.82 %	2.15 %	8.18 %	4 s	1 core @ 3.0 Ghz (Matlab + C/C++)
8	Up-Conv-Poly		code	89.88 %	87.52 %	92.01 %	87.84 %	1.34 %	12.16 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.
9	SPRAY			83.42 %	86.84 %	84.76 %	82.12 %	2.60 %	17.88 %	45 ms	NVIDIA GTX 580 (Python + OpenCL)
T. Kuehnl, F. Kummert and J. Fritsch: Spatial Ray Features for Real-Time Ego-Lane Extraction. Proc. IEEE Intelligent Transportation Systems 2012.
10	YhY		code	75.85 %	79.27 %	71.49 %	80.77 %	5.67 %	19.23 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
11	LKW			75.85 %	79.27 %	71.49 %	80.77 %	5.67 %	19.23 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
12	Strait			70.82 %	74.07 %	65.20 %	77.50 %	7.28 %	22.50 %	69 ms	GPU @ K20
13	SPlane + BL			69.63 %	73.78 %	80.01 %	61.63 %	2.71 %	38.37 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
14	SCRFFPFHGSP			57.22 %	39.34 %	41.78 %	90.79 %	22.28 %	9.21 %	5 s	8 cores @ 2.5 Ghz (C/C++, Matlab)
I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.

Table as LaTeX | Only published Methods

UM_LANE

	Method	Setting	Code	PRE-20	F1-20	HR-20	PRE-30	F1-30	HR-30	PRE-40	F1-40	HR-40	Runtime	Environment
1	DFFA			99.22 %	99.34 %	99.22 %	98.92 %	98.52 %	97.57 %	96.74 %	93.10 %	86.84 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
2	NVLaneNet			99.22 %	99.37 %	99.01 %	99.03 %	98.62 %	97.38 %	96.74 %	93.10 %	82.89 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
3	BIRD			99.10 %	99.08 %	98.87 %	98.79 %	98.19 %	97.38 %	96.70 %	90.70 %	89.33 %	25 ms	GPU @ 2.5 Ghz (Python + C/C++)
4	AILabsLane			99.13 %	99.06 %	98.79 %	99.22 %	97.91 %	96.60 %	96.74 %	87.36 %	86.84 %	0.25 s	GPU @ 2.5 Ghz (C/C++)
5	FCN-GCBs			99.18 %	99.13 %	98.76 %	99.00 %	98.11 %	96.43 %	95.56 %	93.02 %	81.58 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
6	RBNet			99.24 %	99.33 %	99.21 %	98.74 %	97.34 %	95.92 %	95.56 %	87.21 %	81.58 %	0.18 s	GPU @ 2.5 Ghz (Matlab + C/C++)
Z. Chen and Z. Chen: RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection. International Conference on Neural Information Processing 2017.
7	RD			99.12 %	98.87 %	97.71 %	99.07 %	97.93 %	95.58 %	96.70 %	90.70 %	78.67 %	4 s	1 core @ 3.0 Ghz (Matlab + C/C++)
8	Up-Conv-Poly		code	99.06 %	98.84 %	98.45 %	97.57 %	95.27 %	93.14 %	90.11 %	83.72 %	77.63 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.
9	SPRAY			97.58 %	96.74 %	96.38 %	96.59 %	94.16 %	92.06 %	87.64 %	78.57 %	62.16 %	45 ms	NVIDIA GTX 580 (Python + OpenCL)
T. Kuehnl, F. Kummert and J. Fritsch: Spatial Ray Features for Real-Time Ego-Lane Extraction. Proc. IEEE Intelligent Transportation Systems 2012.
10	YhY		code	95.83 %	92.92 %	89.16 %	94.41 %	89.44 %	83.62 %	84.04 %	66.67 %	50.00 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
11	LKW			95.83 %	92.92 %	89.16 %	94.41 %	89.44 %	83.62 %	84.04 %	66.67 %	50.00 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
12	SPlane + BL			95.53 %	92.88 %	91.21 %	91.89 %	87.12 %	74.28 %	79.79 %	47.13 %	0.00 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
13	Strait			94.97 %	91.74 %	88.38 %	89.60 %	81.85 %	73.89 %	67.02 %	48.28 %	32.89 %	69 ms	GPU @ K20
14	SCRFFPFHGSP			94.88 %	87.95 %	82.98 %	87.91 %	78.90 %	71.95 %	60.64 %	43.68 %	38.16 %	5 s	8 cores @ 2.5 Ghz (C/C++, Matlab)
I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.

Table as LaTeX | Only published Methods