Road/Lane Detection Evaluation 2013


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.

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!

Additional information used by the methods
  • Stereo: Method uses left and right (stereo) images
  • Laser Points: Method uses point clouds from Velodyne laser scanner
  • GPS: Method uses GPS information
  • Additional training data: Use of additional data sources for training (see details)

Road Estimation Evaluation

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 RPP 96.04 % 89.77 % 95.61 % 96.48 % 2.02 % 3.52 % 0.16 s GPU (C/C++)
3 KRSF 96.02 % 93.60 % 95.61 % 96.44 % 2.02 % 3.56 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
4 KRS 95.89 % 93.51 % 95.79 % 95.99 % 1.92 % 4.01 % 0.3 s GPU @ 2.5 Ghz (Python)
5 YhY code 95.80 % 89.11 % 94.89 % 96.73 % 2.38 % 3.27 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
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6 SAIT 95.65 % 90.17 % 96.06 % 95.25 % 1.78 % 4.75 % 0.04 s GPU @ 2.5 Ghz (C/C++)
7 TuSimple 95.64 % 93.50 % 95.46 % 95.83 % 2.08 % 4.17 % 0.2s GPU @ 2.5 Ghz (Python)
8 DFFA 95.58 % 89.30 % 95.10 % 96.06 % 2.25 % 3.94 % 0.4 s GPU @ 2.5 Ghz (C/C++)
9 MVnet
This method makes use of Velodyne laser scans.
95.45 % 91.49 % 97.51 % 93.49 % 1.09 % 6.51 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
10 RSNet 95.28 % 92.43 % 95.22 % 95.35 % 2.18 % 4.65 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
11 DCCN
This method makes use of Velodyne laser scans.
95.18 % 92.44 % 94.69 % 95.68 % 2.45 % 4.32 % 25 ms GPU @ 2.5 Ghz (Python + C/C++)
12 SAIT 95.09 % 89.58 % 95.41 % 94.76 % 2.08 % 5.24 % 0.04 s GPU @ 2.5 Ghz (C/C++)
13 TDCac1 CNN 94.86 % 89.62 % 95.45 % 94.28 % 2.05 % 5.72 % .093 s 1 core @ 1.0 Ghz (C/C++)
14 RSNet- 94.84 % 92.83 % 94.32 % 95.37 % 2.62 % 4.63 % 0.07 s GPU @ 2.5 Ghz (Python)
15 baseline 94.80 % 92.80 % 94.35 % 95.25 % 2.60 % 4.75 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
16 RBNet 94.77 % 91.42 % 95.16 % 94.37 % 2.19 % 5.63 % 0.18 s GPU @ 2.5 Ghz (Matlab + C/C++)
17 SN 94.73 % 89.22 % 95.01 % 94.45 % 2.26 % 5.55 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
18 MMN 94.72 % 92.51 % 94.84 % 94.60 % 2.34 % 5.40 % 0.1 s GPU @ 2.5 Ghz (C/C++)
19 KRS 94.69 % 93.40 % 94.72 % 94.67 % 2.41 % 5.33 % 1 s GPU @ 2.5 Ghz (Python)
20 RSNet2 94.65 % 92.54 % 94.45 % 94.85 % 2.54 % 5.15 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
21 RGAN 94.62 % 89.50 % 95.32 % 93.93 % 2.10 % 6.07 % 1 s 1 core @ 2.5 Ghz (C/C++)
22 FNETMS 94.51 % 92.72 % 94.92 % 94.11 % 2.30 % 5.89 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
23 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. Submitted to the Workshop on Deep Learning for Autonomous Driving on IEEE 20th International Conference on Intelligent Transportation Systems 2017.
24 FusionNet
This method uses stereo information.
94.15 % 92.26 % 95.18 % 93.14 % 2.15 % 6.86 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
25 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.
26 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.
27 SSL 93.94 % 89.36 % 94.78 % 93.11 % 2.34 % 6.89 % 0.05 s GPU @ 2.5 Ghz (C/C++)
28 RDBN 93.90 % 91.23 % 94.13 % 93.67 % 2.66 % 6.33 % 0.25 s GPU @ 2.5 Ghz (Matlab + C/C++)
29 CoDNN 93.90 % 92.86 % 94.51 % 93.29 % 2.47 % 6.71 % 0.01 s 1 core @ 2.5 Ghz (Python + C/C++)
30 AXN 93.86 % 92.63 % 94.09 % 93.63 % 2.68 % 6.37 % 0.06 s GPU @ 2.5 Ghz (C/C++)
31 FuseNet
This method uses stereo information.
93.86 % 93.34 % 94.49 % 93.23 % 2.48 % 6.77 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
32 FCN-GCBs 93.86 % 86.62 % 92.15 % 95.62 % 3.71 % 4.38 % 0.08 s GPU @ 2.5 Ghz (C/C++)
33 ResNetPK
This method makes use of Velodyne laser scans.
93.78 % 89.01 % 94.78 % 92.81 % 2.33 % 7.19 % 0.4s GPU @ 1.5 Ghz (Python)
34 MBN 93.77 % 85.59 % 91.02 % 96.69 % 4.34 % 3.31 % 0.16 s GPU @ 2.5 Ghz (Python)
35 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.
36 uickitti 93.27 % 92.92 % 93.20 % 93.34 % 3.10 % 6.66 % 0.1 s GPU @ 1.0 Ghz (Python)
37 s-FCN-loc 92.85 % 87.41 % 93.02 % 92.68 % 3.17 % 7.32 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
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.
38 RDSN 92.77 % 87.54 % 93.16 % 92.39 % 3.09 % 7.61 % 0.25 s GPU @ 2.5 Ghz (Python)
39 LoDNN
This method makes use of Velodyne laser scans.
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.
40 DFN 92.32 % 88.26 % 91.79 % 92.85 % 3.78 % 7.15 % 0.25 s GPU @ >3.5 Ghz (Python)
41 RSNetVGG 92.26 % 92.51 % 93.92 % 90.65 % 2.67 % 9.35 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
42 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.
43 HID-LS
This method makes use of Velodyne laser scans.
92.03 % 83.73 % 88.97 % 95.31 % 5.38 % 4.69 % 0.5 s 4 cores @ 3.0 Ghz (C/C++)
44 FF 91.63 % 87.14 % 92.72 % 90.56 % 3.24 % 9.44 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
45 MixedCRF
This method makes use of Velodyne laser scans.
91.57 % 84.68 % 90.02 % 93.19 % 4.71 % 6.81 % 6s 1 core @ 2.5 Ghz (Matlab + C/C++)
46 JOJnet 91.47 % 87.64 % 92.12 % 90.82 % 3.54 % 9.18 % 0.1 s GPU @ 2.5 Ghz (C/C++)
47 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.
48 HybridCRF
This method makes use of Velodyne laser scans.
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.
49 SGL 90.94 % 86.35 % 91.86 % 90.05 % 3.64 % 9.95 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
50 VGGFCN 90.93 % 83.56 % 88.79 % 93.18 % 5.36 % 6.82 % 0.4 s GPU @ 1.0 Ghz (Python + C/C++)
51 NNP
This method uses stereo information.
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 Feature++
This method uses stereo information.
90.43 % 88.83 % 89.63 % 91.26 % 4.81 % 8.74 % 13 s 4 core @ 2.5 Ghz (Matlab + C/C++)
54 TFSeg 90.12 % 89.85 % 88.00 % 92.36 % 5.74 % 7.64 % 0.07 s GPU @ 1.0 Ghz (Python)
55 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.
56 LidarHisto
This method makes use of Velodyne laser scans.
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.
57 FusedCRF
This method makes use of Velodyne laser scans.
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 BNV
This method uses stereo information.
89.42 % 83.13 % 88.31 % 90.55 % 5.46 % 9.45 % 3 s 2 cores @ 2.5 Ghz (C/C++)
60 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.
61 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.
62 VPD
This method uses stereo information.
88.87 % 82.04 % 93.31 % 84.83 % 2.77 % 15.17 % 4 s 1 core @ 3.0 Ghz (Matlab + C/C++)
63 HFM 88.83 % 80.33 % 85.23 % 92.75 % 7.32 % 7.25 % 5 s 2 cores @ 2.0 Ghz (C/C++)
64 DVFCN 88.64 % 91.37 % 89.20 % 88.10 % 4.86 % 11.90 % 0.07 s GPU @ 2.5 Ghz (Python)
65 ResAXN 88.46 % 91.06 % 90.10 % 86.88 % 4.35 % 13.12 % 0.06 s GPU @ 1.5 Ghz (Python)
66 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.
67 FCNB 87.98 % 77.50 % 87.77 % 88.18 % 5.60 % 11.82 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
68 ProbBoost
This method uses stereo information.
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.
69 LWD 87.39 % 88.85 % 84.99 % 89.92 % 7.24 % 10.08 % 0.07 s GPU @ 2.5 Ghz (Python)
70 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.
71 Multimodal 87.28 % 90.82 % 87.12 % 87.44 % 5.89 % 12.56 % 0.3 s 4 cores @ 2.5 Ghz (Matlab)
72 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.
73 GRES3D+VELO
This method makes use of Velodyne laser scans.
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.
74 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.
75 SPlane + BL
This method uses stereo information.
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.
76 geo+gpr+crf
This method uses stereo information.
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.
77 RES3D-Velo
This method makes use of Velodyne laser scans.
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.
78 SCRFFPFHGSP
This method uses stereo information.
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.
79 RNF 83.71 % 72.64 % 88.14 % 79.71 % 4.89 % 20.29 % .5 s 1 core @ 2.5 Ghz (C/C++)
80 GRES3D+SELAS
This method uses stereo information.
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
This method uses stereo information.
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 PFH+HSV 82.99 % 74.81 % 84.48 % 81.56 % 6.83 % 18.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
84 FCNS 82.97 % 87.01 % 80.84 % 85.21 % 9.20 % 14.79 % 6 s 1 core @ 2.5 Ghz (C/C++)
85 INM 82.56 % 74.66 % 90.92 % 75.61 % 3.44 % 24.39 % 5 s 1 core @ 2.5 Ghz (C/C++)
86 SegNet 82.17 % 76.46 % 84.03 % 80.40 % 6.97 % 19.60 % 0.01 s 8 cores @ 2.5 Ghz (C/C++)
87 LKW 82.01 % 85.26 % 78.83 % 85.46 % 10.46 % 14.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 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++)
89 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.
90 4NP code 79.47 % 85.99 % 78.78 % 80.17 % 9.84 % 19.83 % 0.01 s GPU @ 1.5 Ghz (Matlab)
91 RES3D-Stereo
This method uses stereo information.
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.
92 BM
This method uses stereo information.
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.
93 SPlane
This method uses stereo information.
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.
94 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.
95 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.
96 RFH 76.18 % 61.64 % 68.38 % 86.00 % 18.12 % 14.00 % .5 s 1 core @ 2.5 Ghz (C/C++)
97 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.
98 NSV 73.15 % 69.75 % 61.48 % 90.29 % 25.77 % 9.71 % 5 s GPU @ 3.5 Ghz (Matlab)
99 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.
100 ANN
This method uses stereo information.
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.
101 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 TuSimple 97.62 % 95.53 % 97.41 % 97.82 % 2.86 % 2.18 % 0.2s GPU @ 2.5 Ghz (Python)
2 YhY code 97.42 % 93.08 % 97.15 % 97.68 % 3.15 % 2.32 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
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3 KRSF 97.34 % 95.58 % 97.42 % 97.26 % 2.83 % 2.74 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
4 UNV 97.34 % 94.23 % 97.52 % 97.16 % 2.71 % 2.84 % 1.2 s GPU @ 3.0 Ghz (Python + C/C++)
5 SAIT 97.31 % 92.68 % 96.71 % 97.91 % 3.66 % 2.09 % 0.04 s GPU @ 2.5 Ghz (C/C++)
6 KRS 97.27 % 95.55 % 97.19 % 97.34 % 3.09 % 2.66 % 0.3 s GPU @ 2.5 Ghz (Python)
7 DFFA 97.26 % 92.75 % 96.79 % 97.74 % 3.56 % 2.26 % 0.4 s GPU @ 2.5 Ghz (C/C++)
8 SAIT 97.15 % 93.34 % 97.44 % 96.86 % 2.79 % 3.14 % 0.04 s GPU @ 2.5 Ghz (C/C++)
9 DCCN
This method makes use of Velodyne laser scans.
97.10 % 95.31 % 96.64 % 97.56 % 3.73 % 2.44 % 25 ms GPU @ 2.5 Ghz (Python + C/C++)
10 RPP 97.03 % 92.36 % 96.36 % 97.70 % 4.06 % 2.30 % 0.16 s GPU (C/C++)
11 MVnet
This method makes use of Velodyne laser scans.
96.99 % 93.94 % 98.10 % 95.90 % 2.04 % 4.10 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
12 SN 96.95 % 92.87 % 96.92 % 96.98 % 3.39 % 3.02 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
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 RGAN 96.72 % 92.99 % 97.05 % 96.40 % 3.22 % 3.60 % 1 s 1 core @ 2.5 Ghz (C/C++)
17 RSNet2 96.60 % 95.28 % 96.57 % 96.63 % 3.78 % 3.37 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
18 CoDNN 96.56 % 95.33 % 96.41 % 96.71 % 3.96 % 3.29 % 0.01 s 1 core @ 2.5 Ghz (Python + C/C++)
19 RSNet- 96.40 % 95.34 % 96.59 % 96.22 % 3.74 % 3.78 % 0.07 s GPU @ 2.5 Ghz (Python)
20 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.
21 AXN 96.20 % 95.29 % 95.91 % 96.49 % 4.52 % 3.51 % 0.06 s GPU @ 2.5 Ghz (C/C++)
22 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.
23 MMN 96.15 % 94.98 % 96.07 % 96.22 % 4.33 % 3.78 % 0.1 s GPU @ 2.5 Ghz (C/C++)
24 MBN 96.14 % 91.43 % 95.33 % 96.96 % 5.22 % 3.04 % 0.16 s GPU @ 2.5 Ghz (Python)
25 uickitti 96.11 % 95.44 % 96.08 % 96.14 % 4.31 % 3.86 % 0.1 s GPU @ 1.0 Ghz (Python)
26 TDCac1 CNN 96.11 % 92.62 % 96.65 % 95.57 % 3.64 % 4.43 % .093 s 1 core @ 1.0 Ghz (C/C++)
27 FNETMS 96.08 % 94.95 % 95.56 % 96.60 % 4.93 % 3.40 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
28 RBNet 96.06 % 93.49 % 95.80 % 96.31 % 4.64 % 3.69 % 0.18 s GPU @ 2.5 Ghz (Matlab + C/C++)
29 LoDNN
This method makes use of Velodyne laser scans.
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.
30 SSL 96.01 % 93.25 % 96.43 % 95.59 % 3.89 % 4.41 % 0.05 s GPU @ 2.5 Ghz (C/C++)
31 FusionNet
This method uses stereo information.
96.01 % 94.38 % 95.22 % 96.81 % 5.34 % 3.19 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
32 FF 95.79 % 93.85 % 98.00 % 93.68 % 2.10 % 6.32 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
33 FuseNet
This method uses stereo information.
95.61 % 95.80 % 95.29 % 95.93 % 5.21 % 4.07 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
34 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.
35 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. Submitted to the Workshop on Deep Learning for Autonomous Driving on IEEE 20th International Conference on Intelligent Transportation Systems 2017.
36 ResNetPK
This method makes use of Velodyne laser scans.
95.45 % 92.27 % 96.26 % 94.65 % 4.04 % 5.35 % 0.4s GPU @ 1.5 Ghz (Python)
37 RDSN 95.32 % 91.01 % 94.87 % 95.76 % 5.69 % 4.24 % 0.25 s GPU @ 2.5 Ghz (Python)
38 FCN-GCBs 95.29 % 91.27 % 95.16 % 95.43 % 5.33 % 4.57 % 0.08 s GPU @ 2.5 Ghz (C/C++)
39 RDBN 95.24 % 93.78 % 96.01 % 94.48 % 4.31 % 5.52 % 0.25 s GPU @ 2.5 Ghz (Matlab + C/C++)
40 s-FCN-loc 95.01 % 91.86 % 95.81 % 94.23 % 4.53 % 5.77 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
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.
41 RSNetVGG 94.88 % 95.00 % 95.73 % 94.05 % 4.61 % 5.95 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
42 DFN 94.63 % 92.15 % 94.59 % 94.67 % 5.95 % 5.33 % 0.25 s GPU @ >3.5 Ghz (Python)
43 HID-LS
This method makes use of Velodyne laser scans.
94.36 % 91.01 % 94.88 % 93.84 % 5.57 % 6.16 % 0.5 s 4 cores @ 3.0 Ghz (C/C++)
44 VGGFCN 94.26 % 91.14 % 95.02 % 93.51 % 5.38 % 6.49 % 0.4 s GPU @ 1.0 Ghz (Python + C/C++)
45 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.
46 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.
47 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.
48 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.
49 Feature++
This method uses stereo information.
93.55 % 92.34 % 92.77 % 94.34 % 8.08 % 5.66 % 13 s 4 core @ 2.5 Ghz (Matlab + C/C++)
50 JOJnet 93.42 % 88.88 % 91.93 % 94.96 % 9.16 % 5.04 % 0.1 s GPU @ 2.5 Ghz (C/C++)
51 LidarHisto
This method makes use of Velodyne laser scans.
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.
52 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.
53 HFM 93.12 % 87.10 % 90.58 % 95.82 % 10.96 % 4.18 % 5 s 2 cores @ 2.0 Ghz (C/C++)
54 ResAXN 92.99 % 94.76 % 93.75 % 92.24 % 6.76 % 7.76 % 0.06 s GPU @ 1.5 Ghz (Python)
55 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.
56 LWDS 92.81 % 94.66 % 94.41 % 91.25 % 5.93 % 8.75 % 0.07 s GPU @ 2.5 Ghz (Python)
57 MixedCRF
This method makes use of Velodyne laser scans.
92.75 % 90.24 % 94.03 % 91.50 % 6.39 % 8.50 % 6s 1 core @ 2.5 Ghz (Matlab + C/C++)
58 SGL 92.39 % 87.73 % 95.59 % 89.40 % 4.53 % 10.60 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
59 BNV
This method uses stereo information.
92.21 % 87.99 % 91.55 % 92.89 % 9.43 % 7.11 % 3 s 2 cores @ 2.5 Ghz (C/C++)
60 HybridCRF
This method makes use of Velodyne laser scans.
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 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.
62 TFSeg 91.41 % 93.00 % 91.68 % 91.15 % 9.09 % 8.85 % 0.07 s GPU @ 1.0 Ghz (Python)
63 ProbBoost
This method uses stereo information.
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.
64 NNP
This method uses stereo information.
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.
65 Multimodal 91.31 % 94.23 % 89.75 % 92.92 % 11.67 % 7.08 % 0.3 s 4 cores @ 2.5 Ghz (Matlab)
66 FCNS 91.28 % 93.68 % 90.51 % 92.07 % 10.61 % 7.93 % 6 s 1 core @ 2.5 Ghz (C/C++)
67 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++)
68 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.
69 RES3D-Velo
This method makes use of Velodyne laser scans.
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.
70 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.
71 FCNB 90.21 % 86.28 % 89.67 % 90.77 % 11.50 % 9.23 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
72 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.
73 DVFCN 89.95 % 93.93 % 89.74 % 90.17 % 11.33 % 9.83 % 0.07 s GPU @ 2.5 Ghz (Python)
74 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.
75 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.
76 FusedCRF
This method makes use of Velodyne laser scans.
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.
77 BM
This method uses stereo information.
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.
78 RFH 89.34 % 79.11 % 81.78 % 98.42 % 24.10 % 1.58 % .5 s 1 core @ 2.5 Ghz (C/C++)
79 HistonBoost
This method uses stereo information.
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.
80 SegNet 88.59 % 83.54 % 88.35 % 88.84 % 12.88 % 11.16 % 0.01 s 8 cores @ 2.5 Ghz (C/C++)
81 PFH+HSV 88.39 % 83.32 % 90.20 % 86.65 % 10.35 % 13.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 geo+gpr+crf
This method uses stereo information.
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.
83 GRES3D+VELO
This method makes use of Velodyne laser scans.
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.
84 SCRFFPFHGSP
This method uses stereo information.
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.
85 4NP code 87.75 % 90.75 % 82.54 % 93.67 % 21.78 % 6.33 % 0.01 s GPU @ 1.5 Ghz (Matlab)
86 GRES3D+SELAS
This method uses stereo information.
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.
87 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.
88 LWD 86.14 % 88.31 % 86.99 % 85.31 % 14.03 % 14.69 % 0.07 s GPU @ 2.5 Ghz (Python)
89 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++)
90 RNF 83.87 % 78.26 % 84.02 % 83.72 % 17.51 % 16.28 % .5 s 1 core @ 2.5 Ghz (C/C++)
91 RES3D-Stereo
This method uses stereo information.
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.
92 INM 83.48 % 82.35 % 93.60 % 75.34 % 5.66 % 24.66 % 5 s 1 core @ 2.5 Ghz (C/C++)
93 NSV 83.11 % 80.65 % 73.97 % 94.83 % 36.68 % 5.17 % 5 s GPU @ 3.5 Ghz (Matlab)
94 SPlane
This method uses stereo information.
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.
95 SPlane + BL
This method uses stereo information.
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.
96 ANN
This method uses stereo information.
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.
97 LKW 75.48 % 78.73 % 65.97 % 88.18 % 50.00 % 11.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
98 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 UNV 95.71 % 90.32 % 95.13 % 96.30 % 1.61 % 3.70 % 1.2 s GPU @ 3.0 Ghz (Python + C/C++)
2 DFFA 95.57 % 89.21 % 95.67 % 95.47 % 1.41 % 4.53 % 0.4 s GPU @ 2.5 Ghz (C/C++)
3 KRS 95.55 % 92.90 % 95.81 % 95.30 % 1.36 % 4.70 % 0.3 s GPU @ 2.5 Ghz (Python)
4 KRSF 95.53 % 92.96 % 96.25 % 94.83 % 1.21 % 5.17 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
5 RPP 95.47 % 88.74 % 95.16 % 95.77 % 1.59 % 4.23 % 0.16 s GPU (C/C++)
6 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.
7 TuSimple 95.17 % 92.73 % 95.97 % 94.39 % 1.29 % 5.61 % 0.2s GPU @ 2.5 Ghz (Python)
8 SAIT 95.06 % 87.59 % 93.89 % 96.25 % 2.04 % 3.75 % 0.04 s GPU @ 2.5 Ghz (C/C++)
9 SAIT 95.01 % 88.58 % 94.98 % 95.04 % 1.64 % 4.96 % 0.04 s GPU @ 2.5 Ghz (C/C++)
10 DCCN
This method makes use of Velodyne laser scans.
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 SN 94.54 % 87.70 % 94.01 % 95.09 % 1.97 % 4.91 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
14 RGAN 94.40 % 87.84 % 94.17 % 94.63 % 1.91 % 5.37 % 1 s 1 core @ 2.5 Ghz (C/C++)
15 baseline 94.14 % 92.15 % 93.69 % 94.60 % 2.08 % 5.40 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
16 MVnet
This method makes use of Velodyne laser scans.
94.07 % 89.03 % 95.48 % 92.71 % 1.43 % 7.29 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
17 RSNet2 93.98 % 91.88 % 93.81 % 94.15 % 2.03 % 5.85 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
18 RSNet- 93.90 % 91.69 % 92.94 % 94.88 % 2.35 % 5.12 % 0.07 s GPU @ 2.5 Ghz (Python)
19 MMN 93.87 % 91.23 % 93.26 % 94.48 % 2.22 % 5.52 % 0.1 s GPU @ 2.5 Ghz (C/C++)
20 CoDNN 93.81 % 92.41 % 94.20 % 93.43 % 1.88 % 6.57 % 0.01 s 1 core @ 2.5 Ghz (Python + C/C++)
21 FNETMS 93.71 % 91.79 % 94.08 % 93.34 % 1.91 % 6.66 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
22 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.
23 FF 93.62 % 89.14 % 95.59 % 91.72 % 1.38 % 8.28 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
24 uickitti 93.58 % 92.14 % 93.34 % 93.83 % 2.18 % 6.17 % 0.1 s GPU @ 1.0 Ghz (Python)
25 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.
26 RBNet 93.21 % 89.18 % 92.81 % 93.60 % 2.36 % 6.40 % 0.18 s GPU @ 2.5 Ghz (Matlab + C/C++)
27 SSL 93.19 % 88.17 % 92.99 % 93.41 % 2.30 % 6.59 % 0.05 s GPU @ 2.5 Ghz (C/C++)
28 AXN 92.97 % 91.91 % 92.68 % 93.27 % 2.40 % 6.73 % 0.06 s GPU @ 2.5 Ghz (C/C++)
29 FuseNet
This method uses stereo information.
92.97 % 92.47 % 93.44 % 92.51 % 2.12 % 7.49 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
30 FusionNet
This method uses stereo information.
92.89 % 90.69 % 92.75 % 93.03 % 2.37 % 6.97 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
31 MBN 92.89 % 83.53 % 89.43 % 96.63 % 3.72 % 3.37 % 0.16 s GPU @ 2.5 Ghz (Python)
32 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.
33 ResNetPK
This method makes use of Velodyne laser scans.
92.56 % 86.93 % 93.16 % 91.96 % 2.20 % 8.04 % 0.4s GPU @ 1.5 Ghz (Python)
34 RDBN 92.51 % 89.75 % 92.95 % 92.08 % 2.28 % 7.92 % 0.25 s GPU @ 2.5 Ghz (Matlab + C/C++)
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
This method makes use of Velodyne laser scans.
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 RDSN 91.98 % 85.64 % 91.45 % 92.53 % 2.82 % 7.47 % 0.25 s GPU @ 2.5 Ghz (Python)
39 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.
40 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.
41 RSNetVGG 91.72 % 91.52 % 92.62 % 90.84 % 2.36 % 9.16 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
42 DFN 91.70 % 87.72 % 91.51 % 91.89 % 2.78 % 8.11 % 0.25 s GPU @ >3.5 Ghz (Python)
43 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. Submitted to the Workshop on Deep Learning for Autonomous Driving on IEEE 20th International Conference on Intelligent Transportation Systems 2017.
44 JOJnet 90.72 % 86.26 % 91.32 % 90.13 % 2.79 % 9.87 % 0.1 s GPU @ 2.5 Ghz (C/C++)
45 s-FCN-loc 90.42 % 80.07 % 92.40 % 88.52 % 2.37 % 11.48 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
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.
46 VPD
This method uses stereo information.
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 HFM 89.20 % 80.48 % 86.07 % 92.56 % 4.88 % 7.44 % 5 s 2 cores @ 2.0 Ghz (C/C++)
49 HID-LS
This method makes use of Velodyne laser scans.
89.10 % 80.53 % 86.13 % 92.29 % 4.84 % 7.71 % 0.5 s 4 cores @ 3.0 Ghz (C/C++)
50 VGGFCN 88.90 % 76.87 % 88.49 % 89.31 % 3.79 % 10.69 % 0.4 s GPU @ 1.0 Ghz (Python + C/C++)
51 HybridCRF
This method makes use of Velodyne laser scans.
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.
52 SGL 88.40 % 78.31 % 90.25 % 86.63 % 3.05 % 13.37 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
53 TFSeg 87.42 % 88.58 % 87.28 % 87.56 % 4.16 % 12.44 % 0.07 s GPU @ 1.0 Ghz (Python)
54 Feature++
This method uses stereo information.
87.40 % 85.48 % 86.19 % 88.64 % 4.63 % 11.36 % 13 s 4 core @ 2.5 Ghz (Matlab + C/C++)
55 LidarHisto
This method makes use of Velodyne laser scans.
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 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.
57 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.
58 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.
59 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.
60 MixedCRF
This method makes use of Velodyne laser scans.
85.69 % 75.12 % 80.17 % 92.02 % 7.42 % 7.98 % 6s 1 core @ 2.5 Ghz (Matlab + C/C++)
61 NNP
This method uses stereo information.
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.
62 BNV
This method uses stereo information.
85.46 % 74.07 % 85.06 % 85.86 % 4.91 % 14.14 % 3 s 2 cores @ 2.5 Ghz (C/C++)
63 DVFCN 85.37 % 89.13 % 85.44 % 85.30 % 4.74 % 14.70 % 0.07 s GPU @ 2.5 Ghz (Python)
64 FCNB 85.29 % 73.20 % 84.01 % 86.62 % 5.37 % 13.38 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
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
This method makes use of Velodyne laser scans.
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 GRES3D+VELO
This method makes use of Velodyne laser scans.
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.
70 ResAXN 83.68 % 87.44 % 84.04 % 83.33 % 5.16 % 16.67 % 0.06 s GPU @ 1.5 Ghz (Python)
71 RES3D-Velo
This method makes use of Velodyne laser scans.
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.
72 Multimodal 83.01 % 87.47 % 80.20 % 86.02 % 6.92 % 13.98 % 0.3 s 4 cores @ 2.5 Ghz (Matlab)
73 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.
74 GRES3D+SELAS
This method uses stereo information.
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.
75 geo+gpr+crf
This method uses stereo information.
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.
76 SCRFFPFHGSP
This method uses stereo information.
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.
77 ProbBoost
This method uses stereo information.
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.
78 FCNS 80.20 % 84.90 % 77.92 % 82.62 % 7.63 % 17.38 % 6 s 1 core @ 2.5 Ghz (C/C++)
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 PFH+HSV 79.61 % 66.78 % 82.60 % 76.83 % 5.27 % 23.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 RES3D-Stereo
This method uses stereo information.
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
This method uses stereo information.
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 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.
87 RNF 75.36 % 63.87 % 78.61 % 72.37 % 6.42 % 27.63 % .5 s 1 core @ 2.5 Ghz (C/C++)
88 4NP code 74.60 % 79.21 % 70.52 % 79.19 % 10.79 % 20.81 % 0.01 s GPU @ 1.5 Ghz (Matlab)
89 HistonBoost
This method uses stereo information.
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.
90 SPlane + BL
This method uses stereo information.
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.
91 SPlane
This method uses stereo information.
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.
92 INM 72.90 % 64.53 % 87.36 % 62.55 % 2.95 % 37.45 % 5 s 1 core @ 2.5 Ghz (C/C++)
93 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.
94 RFH 71.69 % 55.20 % 62.01 % 84.96 % 16.96 % 15.04 % .5 s 1 core @ 2.5 Ghz (C/C++)
95 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.
96 LKW 69.65 % 74.02 % 65.45 % 74.42 % 12.80 % 25.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
97 NSV 69.53 % 65.34 % 58.13 % 86.51 % 20.30 % 13.49 % 5 s GPU @ 3.5 Ghz (Matlab)
98 VAP 54.62 % 37.65 % 38.96 % 91.32 % 46.62 % 8.68 % 1 s 1 core @ 2.5 Ghz (Matlab)
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99 ANN
This method uses stereo information.
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 UNV 96.74 % 92.39 % 96.88 % 96.60 % 1.71 % 3.40 % 1.2 s GPU @ 3.0 Ghz (Python + C/C++)
2 KRSF 96.50 % 94.01 % 96.74 % 96.27 % 1.78 % 3.73 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
3 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.
4 KRS 96.41 % 93.96 % 96.43 % 96.38 % 1.96 % 3.62 % 0.3 s GPU @ 2.5 Ghz (Python)
5 TuSimple 96.41 % 93.88 % 96.44 % 96.37 % 1.96 % 3.63 % 0.2s GPU @ 2.5 Ghz (Python)
6 RPP 96.36 % 90.36 % 95.85 % 96.87 % 2.31 % 3.13 % 0.16 s GPU (C/C++)
7 DFFA 96.35 % 90.52 % 96.02 % 96.69 % 2.21 % 3.31 % 0.4 s GPU @ 2.5 Ghz (C/C++)
8 SAIT 96.27 % 90.34 % 95.83 % 96.72 % 2.32 % 3.28 % 0.04 s GPU @ 2.5 Ghz (C/C++)
9 SAIT 96.02 % 90.72 % 96.24 % 95.79 % 2.06 % 4.21 % 0.04 s GPU @ 2.5 Ghz (C/C++)
10 DCCN
This method makes use of Velodyne laser scans.
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
This method makes use of Velodyne laser scans.
95.83 % 91.67 % 97.29 % 94.41 % 1.45 % 5.59 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
13 SN 95.70 % 90.17 % 95.64 % 95.77 % 2.40 % 4.23 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
14 KRS 95.64 % 93.89 % 95.79 % 95.48 % 2.31 % 4.52 % 1 s GPU @ 2.5 Ghz (Python)
15 baseline 95.58 % 93.51 % 95.19 % 95.97 % 2.67 % 4.03 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
16 RGAN 95.53 % 90.35 % 95.84 % 95.24 % 2.28 % 4.76 % 1 s 1 core @ 2.5 Ghz (C/C++)
17 RSNet2 95.35 % 93.32 % 95.20 % 95.49 % 2.65 % 4.51 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
18 RSNet- 95.29 % 93.37 % 94.96 % 95.62 % 2.80 % 4.38 % 0.07 s GPU @ 2.5 Ghz (Python)
19 MMN 95.12 % 92.99 % 94.82 % 95.42 % 2.87 % 4.58 % 0.1 s GPU @ 2.5 Ghz (C/C++)
20 CoDNN 95.06 % 93.58 % 95.24 % 94.88 % 2.61 % 5.12 % 0.01 s 1 core @ 2.5 Ghz (Python + C/C++)
21 FNETMS 94.99 % 93.18 % 94.90 % 95.09 % 2.82 % 4.91 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
22 RBNet 94.97 % 91.49 % 94.94 % 95.01 % 2.79 % 4.99 % 0.18 s GPU @ 2.5 Ghz (Matlab + C/C++)
23 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.
24 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.
25 TDCac1 CNN 94.81 % 89.82 % 95.25 % 94.38 % 2.59 % 5.62 % .093 s 1 core @ 1.0 Ghz (C/C++)
26 AXN 94.69 % 93.36 % 94.70 % 94.69 % 2.92 % 5.31 % 0.06 s GPU @ 2.5 Ghz (C/C++)
27 SSL 94.69 % 90.51 % 94.89 % 94.49 % 2.81 % 5.51 % 0.05 s GPU @ 2.5 Ghz (C/C++)
28 FusionNet
This method uses stereo information.
94.67 % 92.54 % 94.73 % 94.61 % 2.90 % 5.39 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
29 uickitti 94.63 % 93.56 % 94.38 % 94.88 % 3.11 % 5.12 % 0.1 s GPU @ 1.0 Ghz (Python)
30 MBN 94.63 % 87.37 % 92.55 % 96.80 % 4.29 % 3.20 % 0.16 s GPU @ 2.5 Ghz (Python)
31 FuseNet
This method uses stereo information.
94.41 % 93.77 % 94.70 % 94.11 % 2.90 % 5.89 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
32 ResNetPK
This method makes use of Velodyne laser scans.
94.25 % 89.66 % 95.07 % 93.45 % 2.67 % 6.55 % 0.4s GPU @ 1.5 Ghz (Python)
33 RDBN 94.15 % 91.74 % 94.88 % 93.44 % 2.78 % 6.56 % 0.25 s GPU @ 2.5 Ghz (Matlab + C/C++)
34 FCN-GCBs 94.08 % 87.66 % 92.87 % 95.32 % 4.03 % 4.68 % 0.08 s GPU @ 2.5 Ghz (C/C++)
35 LoDNN
This method makes use of Velodyne laser scans.
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.
36 FF 94.02 % 90.34 % 95.82 % 92.28 % 2.22 % 7.72 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
37 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. Submitted to the Workshop on Deep Learning for Autonomous Driving on IEEE 20th International Conference on Intelligent Transportation Systems 2017.
38 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.
39 RDSN 93.75 % 88.33 % 93.55 % 93.96 % 3.57 % 6.04 % 0.25 s GPU @ 2.5 Ghz (Python)
40 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.
41 s-FCN-loc 93.26 % 88.83 % 94.16 % 92.39 % 3.16 % 7.61 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
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.
42 RSNetVGG 93.24 % 93.10 % 94.68 % 91.84 % 2.85 % 8.16 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
43 DFN 93.23 % 89.59 % 93.11 % 93.35 % 3.81 % 6.65 % 0.25 s GPU @ >3.5 Ghz (Python)
44 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.
45 HID-LS
This method makes use of Velodyne laser scans.
92.36 % 85.83 % 90.86 % 93.90 % 5.21 % 6.10 % 0.5 s 4 cores @ 3.0 Ghz (C/C++)
46 JOJnet 92.19 % 87.56 % 91.71 % 92.68 % 4.61 % 7.32 % 0.1 s GPU @ 2.5 Ghz (C/C++)
47 VGGFCN 91.95 % 86.42 % 91.51 % 92.40 % 4.72 % 7.61 % 0.4 s GPU @ 1.0 Ghz (Python + C/C++)
48 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.
49 Feature++
This method uses stereo information.
91.12 % 89.51 % 90.16 % 92.10 % 5.54 % 7.90 % 13 s 4 core @ 2.5 Ghz (Matlab + C/C++)
50 SGL 90.99 % 82.67 % 93.15 % 88.92 % 3.60 % 11.08 % 0.01 s 1 core @ 2.5 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
This method makes use of Velodyne laser scans.
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
This method makes use of Velodyne laser scans.
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
This method makes use of Velodyne laser scans.
90.59 % 84.24 % 89.11 % 92.13 % 6.20 % 7.87 % 6s 1 core @ 2.5 Ghz (Matlab + C/C++)
57 LWDS 89.83 % 87.04 % 91.61 % 88.12 % 4.45 % 11.88 % 0.07 s GPU @ 2.5 Ghz (Python)
58 BNV
This method uses stereo information.
89.75 % 84.15 % 89.02 % 90.49 % 6.15 % 9.51 % 3 s 2 cores @ 2.5 Ghz (C/C++)
59 NNP
This method uses stereo information.
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.
60 TFSeg 89.65 % 89.24 % 88.79 % 90.52 % 6.30 % 9.48 % 0.07 s GPU @ 1.0 Ghz (Python)
61 ResAXN 89.39 % 91.91 % 90.84 % 87.98 % 4.89 % 12.02 % 0.06 s GPU @ 1.5 Ghz (Python)
62 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.
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 FCNB 88.35 % 78.22 % 87.71 % 88.99 % 6.87 % 11.01 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
65 DVFCN 88.34 % 91.70 % 88.51 % 88.17 % 6.30 % 11.83 % 0.07 s GPU @ 2.5 Ghz (Python)
66 FusedCRF
This method makes use of Velodyne laser scans.
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.
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 Multimodal 87.78 % 91.25 % 86.40 % 89.21 % 7.74 % 10.79 % 0.3 s 4 cores @ 2.5 Ghz (Matlab)
69 ProbBoost
This method uses stereo information.
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.
70 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.
71 RES3D-Velo
This method makes use of Velodyne laser scans.
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
This method makes use of Velodyne laser scans.
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 FCNS 85.93 % 89.80 % 84.59 % 87.32 % 8.76 % 12.68 % 6 s 1 core @ 2.5 Ghz (C/C++)
75 LWD 85.74 % 84.86 % 83.76 % 87.81 % 9.38 % 12.19 % 0.07 s GPU @ 2.5 Ghz (Python)
76 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.
77 geo+gpr+crf
This method uses stereo information.
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.
78 GRES3D+SELAS
This method uses stereo information.
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.
79 SCRFFPFHGSP
This method uses stereo information.
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.
80 PFH+HSV 84.67 % 77.38 % 86.68 % 82.76 % 7.00 % 17.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
81 SegNet 84.04 % 78.76 % 85.50 % 82.63 % 7.72 % 17.37 % 0.01 s 8 cores @ 2.5 Ghz (C/C++)
82 HistonBoost
This method uses stereo information.
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.
83 BM
This method uses stereo information.
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.
84 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.
85 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++)
86 RNF 81.79 % 70.72 % 83.92 % 79.77 % 8.42 % 20.23 % .5 s 1 core @ 2.5 Ghz (C/C++)
87 4NP code 81.34 % 86.25 % 77.30 % 85.81 % 13.88 % 14.19 % 0.01 s GPU @ 1.5 Ghz (Matlab)
88 RES3D-Stereo
This method uses stereo information.
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.
89 RFH 80.94 % 69.22 % 72.59 % 91.46 % 19.03 % 8.54 % .5 s 1 core @ 2.5 Ghz (C/C++)
90 INM 80.74 % 76.15 % 91.39 % 72.32 % 3.75 % 27.68 % 5 s 1 core @ 2.5 Ghz (C/C++)
91 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.
92 SPlane + BL
This method uses stereo information.
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.
93 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.
94 SPlane
This method uses stereo information.
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.
95 NSV 76.23 % 72.67 % 65.46 % 91.24 % 26.51 % 8.76 % 5 s GPU @ 3.5 Ghz (Matlab)
96 LKW 75.53 % 79.80 % 69.68 % 82.44 % 19.75 % 17.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
97 ANN
This method uses stereo information.
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.
98 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

Lane Estimation Evaluation

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++)
2 SAIT 93.25 % 87.94 % 95.24 % 91.34 % 0.80 % 8.66 % 0.04 s GPU @ 2.5 Ghz (C/C++)
3 DCCN
This method makes use of Velodyne laser scans.
92.70 % 90.94 % 92.39 % 93.01 % 1.35 % 6.99 % 25 ms GPU @ 2.5 Ghz (Python + C/C++)
4 SAIT 92.29 % 88.39 % 93.31 % 91.28 % 1.15 % 8.72 % 0.04 s GPU @ 2.5 Ghz (C/C++)
5 SSL 92.29 % 88.39 % 93.31 % 91.28 % 1.15 % 8.72 % 0.05 s GPU @ 2.5 Ghz (C/C++)
6 NVLaneNet 91.86 % 91.42 % 90.89 % 92.85 % 1.64 % 7.15 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
7 S-Lane 91.76 % 82.37 % 95.96 % 87.92 % 0.65 % 12.08 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
8 RDBN 91.55 % 81.62 % 94.27 % 88.98 % 0.95 % 11.02 % 0.25 s GPU @ 2.5 Ghz (Matlab + C/C++)
9 FCN-GCBs 91.24 % 85.14 % 92.15 % 90.35 % 1.35 % 9.65 % 0.08 s GPU @ 2.5 Ghz (C/C++)
10 RBNet 90.54 % 82.03 % 94.92 % 86.56 % 0.82 % 13.44 % 0.18 s GPU @ 2.5 Ghz (Matlab + C/C++)
11 JOJnet 90.49 % 83.84 % 89.97 % 91.01 % 1.79 % 8.99 % 0.1 s GPU @ 2.5 Ghz (C/C++)
12 VPD
This method uses stereo information.
90.01 % 81.60 % 88.26 % 91.82 % 2.15 % 8.18 % 4 s 1 core @ 3.0 Ghz (Matlab + C/C++)
13 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.
14 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.
15 FCNS 76.31 % 81.57 % 77.88 % 74.81 % 3.74 % 25.19 % 6 s 1 core @ 2.5 Ghz (C/C++)
16 FCNB 75.85 % 79.27 % 71.49 % 80.77 % 5.67 % 19.23 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
17 PFH+HSV 75.85 % 79.27 % 71.49 % 80.77 % 5.67 % 19.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
18 RPP 75.85 % 79.27 % 71.49 % 80.77 % 5.67 % 19.23 % 0.16 s GPU (C/C++)
19 FF 75.85 % 79.27 % 71.49 % 80.77 % 5.67 % 19.23 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
20 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.
21 LKW 75.85 % 79.27 % 71.49 % 80.77 % 5.67 % 19.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
22 SPlane + BL
This method uses stereo information.
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.
23 SCRFFPFHGSP
This method uses stereo information.
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

Behaviour Evaluation

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++)
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 DCCN
This method makes use of Velodyne laser scans.
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 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++)
5 SAIT 99.23 % 99.32 % 99.13 % 98.71 % 97.41 % 95.93 % 95.65 % 87.36 % 80.26 % 0.04 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++)
7 SAIT 99.21 % 99.24 % 99.08 % 98.83 % 97.34 % 95.85 % 95.65 % 81.61 % 78.95 % 0.04 s GPU @ 2.5 Ghz (C/C++)
8 SSL 99.21 % 99.24 % 99.08 % 98.83 % 97.34 % 95.85 % 95.65 % 81.61 % 78.95 % 0.05 s GPU @ 2.5 Ghz (C/C++)
9 VPD
This method uses stereo information.
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++)
10 S-Lane 99.23 % 99.43 % 99.38 % 98.30 % 96.82 % 95.22 % 93.33 % 87.06 % 81.33 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
11 RDBN 99.19 % 99.32 % 99.21 % 98.05 % 96.48 % 95.07 % 94.51 % 84.88 % 77.63 % 0.25 s GPU @ 2.5 Ghz (Matlab + C/C++)
12 JOJnet 99.18 % 99.26 % 98.94 % 98.10 % 96.38 % 95.03 % 93.48 % 86.21 % 84.21 % 0.1 s GPU @ 2.5 Ghz (C/C++)
13 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.
14 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.
15 FCNS 98.72 % 98.29 % 97.58 % 94.67 % 90.59 % 87.28 % 82.35 % 66.67 % 64.29 % 6 s 1 core @ 2.5 Ghz (C/C++)
16 FCNB 95.83 % 92.92 % 89.16 % 94.41 % 89.44 % 83.62 % 84.04 % 66.67 % 50.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
17 PFH+HSV 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++)
18 RPP 95.83 % 92.92 % 89.16 % 94.41 % 89.44 % 83.62 % 84.04 % 66.67 % 50.00 % 0.16 s GPU (C/C++)
19 FF 95.83 % 92.92 % 89.16 % 94.41 % 89.44 % 83.62 % 84.04 % 66.67 % 50.00 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
20 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.
21 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++)
22 SPlane + BL
This method uses stereo information.
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.
23 SCRFFPFHGSP
This method uses stereo information.
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

Related Datasets

Citation

When using this dataset in your research, we will be happy if you cite us:
@INPROCEEDINGS{Fritsch2013ITSC,
  author = {Jannik Fritsch and Tobias Kuehnl and Andreas Geiger},
  title = {A New Performance Measure and Evaluation Benchmark for Road Detection Algorithms},
  booktitle = {International Conference on Intelligent Transportation Systems (ITSC)},
  year = {2013}
}



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