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 ZongNet 96.70 % 90.12 % 96.00 % 97.41 % 1.85 % 2.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 UNV 96.69 % 92.41 % 97.38 % 96.01 % 1.18 % 3.99 % 1.2 s GPU @ 3.0 Ghz (Python + C/C++)
3 iDST-VT 96.66 % 93.65 % 96.55 % 96.76 % 1.57 % 3.24 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
4 RockyNet 96.65 % 90.09 % 95.97 % 97.35 % 1.86 % 2.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
5 VGGFCN-6D
This method makes use of Velodyne laser scans.
96.64 % 92.85 % 96.37 % 96.91 % 1.66 % 3.09 % .006 s GPU @ 3.5 Ghz (Python)
6 NF2CNN
This method makes use of Velodyne laser scans.
96.09 % 88.40 % 94.11 % 98.16 % 2.80 % 1.84 % .006 s GPU @ 3.5 Ghz (Python)
7 lkl_net 96.04 % 89.10 % 94.88 % 97.22 % 2.39 % 2.78 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
8 KRSF 96.02 % 93.60 % 95.61 % 96.44 % 2.02 % 3.56 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
9 KRS 95.89 % 93.51 % 95.79 % 95.99 % 1.92 % 4.01 % 0.3 s GPU @ 2.5 Ghz (Python)
10 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.
11 LidCamNet
This method makes use of Velodyne laser scans.
95.62 % 93.54 % 95.77 % 95.48 % 1.92 % 4.52 % 0.15 s GPU @ 2.5 Ghz (Python)
12 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.
13 ZmNet 95.49 % 89.72 % 95.56 % 95.42 % 2.02 % 4.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
14 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++)
15 RSNet 95.28 % 92.43 % 95.22 % 95.35 % 2.18 % 4.65 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
16 WNet 95.24 % 93.04 % 96.01 % 94.48 % 1.79 % 5.52 % 0.1 s 4 cores @ 2.5 Ghz (Python)
17 BIRD
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++)
18 SeResNext+densefcn 95.04 % 88.82 % 94.57 % 95.51 % 2.50 % 4.49 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
19 TDCac1 CNN 94.86 % 89.62 % 95.45 % 94.28 % 2.05 % 5.72 % .093 s 1 core @ 1.0 Ghz (C/C++)
20 RSNet- 94.84 % 92.83 % 94.32 % 95.37 % 2.62 % 4.63 % 0.07 s GPU @ 2.5 Ghz (Python)
21 baseline 94.80 % 92.80 % 94.35 % 95.25 % 2.60 % 4.75 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
22 IDA-Fusion
This method makes use of Velodyne laser scans.
94.77 % 88.03 % 93.71 % 95.86 % 2.93 % 4.14 % 0.1 s 4 cores @ 3.5 Ghz (C/C++)
23 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.
24 WSLGAN 94.73 % 89.22 % 95.01 % 94.45 % 2.26 % 5.55 % 800ms GPU @ 1.5 Ghz (Python)
25 MMN 94.72 % 92.51 % 94.84 % 94.60 % 2.34 % 5.40 % 0.1 s GPU @ 2.5 Ghz (C/C++)
26 KRS 94.69 % 93.40 % 94.72 % 94.67 % 2.41 % 5.33 % 1 s GPU @ 2.5 Ghz (Python)
27 RSNet2 94.65 % 92.54 % 94.45 % 94.85 % 2.54 % 5.15 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
28 SSLGAN 94.62 % 89.50 % 95.32 % 93.93 % 2.10 % 6.07 % 700ms GPU @ 1.5 Ghz (Python)
X. Han, J. Lu, C. Zhao, S. You and H. Li: Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks. IEEE Signal Processing Letters 2018.
29 MuNet 94.57 % 88.61 % 94.24 % 94.90 % 2.64 % 5.10 % 0..8 s 1 core @ 2.5 Ghz (C/C++)
30 FNETMS 94.51 % 92.72 % 94.92 % 94.11 % 2.30 % 5.89 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
31 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.
32 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++)
33 SPN 94.06 % 89.64 % 95.47 % 92.68 % 2.00 % 7.32 % 1 s 1 core @ 2.5 Ghz (C/C++)
34 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.
35 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.
36 FDN 93.99 % 93.29 % 94.53 % 93.46 % 2.47 % 6.54 % 0.2 s GPU 1 core @ 2.5 Ghz (Python)
37 CoDNN 93.90 % 92.86 % 94.51 % 93.29 % 2.47 % 6.71 % 0.01 s 1 core @ 2.5 Ghz (Python + C/C++)
38 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++)
39 FCN-GCBs 93.86 % 86.62 % 92.15 % 95.62 % 3.71 % 4.38 % 0.08 s GPU @ 2.5 Ghz (C/C++)
40 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)
41 FCN_RGBD
This method uses stereo information.
93.78 % 93.40 % 94.71 % 92.86 % 2.36 % 7.14 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
42 MBN 93.77 % 85.59 % 91.02 % 96.69 % 4.34 % 3.31 % 0.16 s GPU @ 2.5 Ghz (Python)
43 ChipNet
This method makes use of Velodyne laser scans.
93.73 % 87.62 % 93.25 % 94.21 % 3.11 % 5.79 % 12 ms GPU @ 1.5 Ghz (Keras)
44 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.
45 THISV-005 93.50 % 93.26 % 96.82 % 90.41 % 1.35 % 9.59 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
46 THISV-004 93.32 % 93.20 % 96.46 % 90.38 % 1.51 % 9.62 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
47 wt 93.27 % 92.92 % 93.20 % 93.34 % 3.10 % 6.66 % 0.1 s GPU @ 1.0 Ghz (Python)
48 UView 93.15 % 86.18 % 91.67 % 94.67 % 3.92 % 5.33 % 0.2 s GPU @ 1.0 Ghz (Python)
49 HID-LS
This method makes use of Velodyne laser scans.
93.10 % 86.38 % 91.89 % 94.33 % 3.79 % 5.67 % 0.25 s 4 cores @ 3.0 Ghz (C/C++)
50 RDSN 92.77 % 87.54 % 93.16 % 92.39 % 3.09 % 7.61 % 0.25 s GPU @ 2.5 Ghz (Python)
51 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.
52 THISV-003 92.69 % 92.76 % 96.11 % 89.51 % 1.65 % 10.49 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
53 SUNet 92.63 % 86.51 % 92.03 % 93.24 % 3.68 % 6.76 % 0.018s
54 DFN 92.32 % 88.26 % 91.79 % 92.85 % 3.78 % 7.15 % 0.25 s GPU @ >3.5 Ghz (Python)
55 RSNetVGG 92.26 % 92.51 % 93.92 % 90.65 % 2.67 % 9.35 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
56 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.
57 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++)
X. Han, H. Wang, J. Lu and C. Zhao: Road detection based on the fusion of Lidar and image data. 2017.
58 fcn_rgbd 91.25 % 92.30 % 89.86 % 92.68 % 4.77 % 7.32 % 0.6 s 1 core @ 2.5 Ghz (C/C++)
59 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.
60 THISV-002 91.09 % 92.63 % 92.56 % 89.67 % 3.28 % 10.33 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
61 test-001 91.09 % 92.63 % 92.56 % 89.67 % 3.28 % 10.33 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
62 SaimFCN 91.03 % 84.64 % 89.98 % 92.11 % 4.67 % 7.89 % tba s 1 core @ 2.5 Ghz (C/C++)
63 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 2018.
64 tFSSNet 90.83 % 83.99 % 89.26 % 92.46 % 5.07 % 7.54 % 0.02 s 1 core @ 2.5 Ghz (Python)
65 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.
66 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.
67 TFSeg 90.12 % 89.85 % 88.00 % 92.36 % 5.74 % 7.64 % 0.07 s GPU @ 1.0 Ghz (Python)
68 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.
69 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.
70 CNN-FCRF 89.87 % 83.38 % 88.58 % 91.18 % 5.35 % 8.82 % 1 s 4 cores @ 3.5 Ghz (C/C++)
71 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.
72 LWDS 89.50 % 86.11 % 90.59 % 88.44 % 4.19 % 11.56 % 0.07 s GPU @ 2.5 Ghz (Python)
73 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.
74 PAGM code 89.08 % 78.11 % 88.52 % 89.66 % 5.30 % 10.34 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
75 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.
76 RD 88.87 % 82.04 % 93.31 % 84.83 % 2.77 % 15.17 % 4 s 1 core @ 3.0 Ghz (Matlab + C/C++)
77 HFM 88.83 % 80.33 % 85.23 % 92.75 % 7.32 % 7.25 % 5 s 2 cores @ 2.0 Ghz (C/C++)
78 DVFCN 88.64 % 91.37 % 89.20 % 88.10 % 4.86 % 11.90 % 0.07 s GPU @ 2.5 Ghz (Python)
79 ResAXN 88.46 % 91.06 % 90.10 % 86.88 % 4.35 % 13.12 % 0.06 s GPU @ 1.5 Ghz (Python)
80 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.
81 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.
82 LWD 87.39 % 88.85 % 84.99 % 89.92 % 7.24 % 10.08 % 0.07 s GPU @ 2.5 Ghz (Python)
83 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.
84 RoadNet-v2 86.74 % 90.43 % 88.09 % 85.44 % 5.26 % 14.56 % 10 GPU(GTX950M) @ 0.9 Ghz (Keras+Tensorflo)
85 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.
86 PAGM_t code 86.14 % 74.80 % 84.47 % 87.88 % 7.36 % 12.12 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
87 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.
88 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.
89 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.
90 CNN+BiGRU 85.21 % 79.80 % 90.57 % 80.44 % 3.82 % 19.56 % 20 ms GPU @ GTX950M (Python +Tensorflow)
91 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.
92 CNN_LSTM 85.09 % 78.98 % 89.57 % 81.03 % 4.30 % 18.97 % 20 ms GPU @ GTX950M (Python +Tensorflow)
93 Strait 84.28 % 87.89 % 83.66 % 84.91 % 7.56 % 15.09 % 69 ms GPU @ K20
94 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.
95 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.
96 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.
97 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.
98 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++)
99 SegNet 82.17 % 76.46 % 84.03 % 80.40 % 6.97 % 19.60 % 0.01 s 8 cores @ 2.5 Ghz (C/C++)
100 LKW 82.01 % 85.26 % 78.83 % 85.46 % 10.46 % 14.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
101 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++)
102 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.
103 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.
104 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.
105 CNN_LSTM 78.42 % 69.11 % 83.28 % 74.09 % 6.78 % 25.91 % 54 ms GPU @ 2.0 Ghz (Python + C/C++)
106 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.
107 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.
108 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.
109 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.
110 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.
111 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.
112 VAP 59.23 % 42.05 % 44.44 % 88.75 % 50.55 % 11.25 % 1 s 1 core @ 2.5 Ghz (Matlab)
ERROR: Wrong syntax in BIBTEX file.
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
This method makes use of Velodyne laser scans.
97.78 % 95.34 % 97.76 % 97.80 % 2.47 % 2.20 % .006 s GPU @ 3.5 Ghz (Python)
3 NF2CNN
This method makes use of Velodyne laser scans.
97.77 % 93.31 % 97.41 % 98.13 % 2.87 % 1.87 % .006 s GPU @ 3.5 Ghz (Python)
4 RockyNet 97.53 % 93.05 % 97.12 % 97.94 % 3.19 % 2.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
5 ZongNet 97.53 % 93.05 % 97.12 % 97.94 % 3.19 % 2.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
6 ZmNet 97.46 % 92.71 % 96.75 % 98.19 % 3.63 % 1.81 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
7 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.
8 KRSF 97.34 % 95.58 % 97.42 % 97.26 % 2.83 % 2.74 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
9 UNV 97.34 % 94.23 % 97.52 % 97.16 % 2.71 % 2.84 % 1.2 s GPU @ 3.0 Ghz (Python + C/C++)
10 KRS 97.27 % 95.55 % 97.19 % 97.34 % 3.09 % 2.66 % 0.3 s GPU @ 2.5 Ghz (Python)
11 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.
12 BIRD
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++)
13 LidCamNet
This method makes use of Velodyne laser scans.
97.08 % 95.51 % 97.28 % 96.88 % 2.98 % 3.12 % 0.15 s GPU @ 2.5 Ghz (Python)
14 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++)
15 WSLGAN 96.95 % 92.87 % 96.92 % 96.98 % 3.39 % 3.02 % 800ms GPU @ 1.5 Ghz (Python)
16 baseline 96.88 % 95.39 % 96.36 % 97.40 % 4.04 % 2.60 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
17 RSNet 96.85 % 95.26 % 96.79 % 96.91 % 3.54 % 3.09 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
18 KRS 96.84 % 95.49 % 96.70 % 96.98 % 3.64 % 3.02 % 1 s GPU @ 2.5 Ghz (Python)
19 lkl_net 96.82 % 91.31 % 95.21 % 98.48 % 5.45 % 1.52 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
20 SPN 96.81 % 92.89 % 96.94 % 96.67 % 3.36 % 3.33 % 1 s 1 core @ 2.5 Ghz (C/C++)
21 SeResNext+densefcn 96.78 % 91.19 % 95.07 % 98.56 % 5.62 % 1.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
22 SSLGAN 96.72 % 92.99 % 97.05 % 96.40 % 3.22 % 3.60 % 700ms GPU @ 1.5 Ghz (Python)
X. Han, J. Lu, C. Zhao, S. You and H. Li: Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks. IEEE Signal Processing Letters 2018.
23 WNet 96.63 % 94.96 % 96.66 % 96.60 % 3.67 % 3.40 % 0.1 s 4 cores @ 2.5 Ghz (Python)
24 RSNet2 96.60 % 95.28 % 96.57 % 96.63 % 3.78 % 3.37 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
25 CoDNN 96.56 % 95.33 % 96.41 % 96.71 % 3.96 % 3.29 % 0.01 s 1 core @ 2.5 Ghz (Python + C/C++)
26 RSNet- 96.40 % 95.34 % 96.59 % 96.22 % 3.74 % 3.78 % 0.07 s GPU @ 2.5 Ghz (Python)
27 IDA-Fusion
This method makes use of Velodyne laser scans.
96.37 % 92.66 % 96.69 % 96.06 % 3.61 % 3.94 % 0.1 s 4 cores @ 3.5 Ghz (C/C++)
28 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.
29 MuNet 96.20 % 92.44 % 96.31 % 96.09 % 4.05 % 3.91 % 0..8 s 1 core @ 2.5 Ghz (C/C++)
30 FDN 96.19 % 95.26 % 95.49 % 96.89 % 5.03 % 3.11 % 0.2 s GPU 1 core @ 2.5 Ghz (Python)
31 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.
32 MMN 96.15 % 94.98 % 96.07 % 96.22 % 4.33 % 3.78 % 0.1 s GPU @ 2.5 Ghz (C/C++)
33 MBN 96.14 % 91.43 % 95.33 % 96.96 % 5.22 % 3.04 % 0.16 s GPU @ 2.5 Ghz (Python)
34 THISV-005 96.13 % 95.44 % 96.49 % 95.78 % 3.83 % 4.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
35 wt 96.11 % 95.44 % 96.08 % 96.14 % 4.31 % 3.86 % 0.1 s GPU @ 1.0 Ghz (Python)
36 TDCac1 CNN 96.11 % 92.62 % 96.65 % 95.57 % 3.64 % 4.43 % .093 s 1 core @ 1.0 Ghz (C/C++)
37 THISV-004 96.10 % 95.45 % 96.47 % 95.73 % 3.85 % 4.27 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
38 FNETMS 96.08 % 94.95 % 95.56 % 96.60 % 4.93 % 3.40 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
39 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.
40 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.
41 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++)
42 THISV-002 95.88 % 95.41 % 95.48 % 96.28 % 5.01 % 3.72 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
43 THISV-003 95.85 % 95.49 % 96.74 % 94.98 % 3.52 % 5.02 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
44 UView 95.78 % 92.02 % 95.26 % 96.31 % 5.26 % 3.69 % 0.2 s GPU @ 1.0 Ghz (Python)
45 tFSSNet 95.78 % 90.32 % 94.11 % 97.51 % 6.71 % 2.49 % 0.02 s 1 core @ 2.5 Ghz (Python)
46 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++)
47 FCN_RGBD
This method uses stereo information.
95.55 % 95.46 % 95.67 % 95.44 % 4.75 % 4.56 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
48 test-001 95.55 % 95.33 % 95.45 % 95.65 % 5.01 % 4.35 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
49 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.
50 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.
51 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)
52 RDSN 95.32 % 91.01 % 94.87 % 95.76 % 5.69 % 4.24 % 0.25 s GPU @ 2.5 Ghz (Python)
53 FCN-GCBs 95.29 % 91.27 % 95.16 % 95.43 % 5.33 % 4.57 % 0.08 s GPU @ 2.5 Ghz (C/C++)
54 SUNet 95.13 % 91.55 % 95.46 % 94.80 % 4.95 % 5.20 % 0.018s
55 CNN-FCRF 95.10 % 89.03 % 92.69 % 97.62 % 8.46 % 2.38 % 1 s 4 cores @ 3.5 Ghz (C/C++)
56 HID-LS
This method makes use of Velodyne laser scans.
94.89 % 91.46 % 95.37 % 94.42 % 5.04 % 5.58 % 0.25 s 4 cores @ 3.0 Ghz (C/C++)
57 RSNetVGG 94.88 % 95.00 % 95.73 % 94.05 % 4.61 % 5.95 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
58 ChipNet
This method makes use of Velodyne laser scans.
94.87 % 91.31 % 95.21 % 94.53 % 5.23 % 5.47 % 12 ms GPU @ 1.5 Ghz (Keras)
59 DFN 94.63 % 92.15 % 94.59 % 94.67 % 5.95 % 5.33 % 0.25 s GPU @ >3.5 Ghz (Python)
60 fcn_rgbd 94.57 % 95.15 % 93.58 % 95.57 % 7.21 % 4.43 % 0.6 s 1 core @ 2.5 Ghz (C/C++)
61 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.
62 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.
63 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.
64 SaimFCN 93.68 % 89.74 % 93.48 % 93.87 % 7.20 % 6.13 % tba s 1 core @ 2.5 Ghz (C/C++)
65 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.
66 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.
67 PAGM code 93.29 % 91.04 % 94.91 % 91.72 % 5.41 % 8.28 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
68 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.
69 RoadNet-v2 93.17 % 94.45 % 91.36 % 95.06 % 9.88 % 4.94 % 10 GPU(GTX950M) @ 0.9 Ghz (Keras+Tensorflo)
70 HFM 93.12 % 87.10 % 90.58 % 95.82 % 10.96 % 4.18 % 5 s 2 cores @ 2.0 Ghz (C/C++)
71 ResAXN 92.99 % 94.76 % 93.75 % 92.24 % 6.76 % 7.76 % 0.06 s GPU @ 1.5 Ghz (Python)
72 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.
73 LWDS 92.81 % 94.66 % 94.41 % 91.25 % 5.93 % 8.75 % 0.07 s GPU @ 2.5 Ghz (Python)
74 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++)
X. Han, H. Wang, J. Lu and C. Zhao: Road detection based on the fusion of Lidar and image data. 2017.
75 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 2018.
76 CNN_LSTM 91.81 % 86.27 % 93.80 % 89.89 % 6.53 % 10.11 % 20 ms GPU @ GTX950M (Python +Tensorflow)
77 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.
78 TFSeg 91.41 % 93.00 % 91.68 % 91.15 % 9.09 % 8.85 % 0.07 s GPU @ 1.0 Ghz (Python)
79 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.
80 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.
81 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++)
82 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.
83 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.
84 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.
85 PAGM_t code 90.20 % 84.47 % 91.60 % 88.84 % 8.95 % 11.16 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
86 CNN+BiGRU 90.02 % 85.49 % 92.84 % 87.36 % 7.40 % 12.64 % 20 ms GPU @ GTX950M (Python +Tensorflow)
87 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.
88 DVFCN 89.95 % 93.93 % 89.74 % 90.17 % 11.33 % 9.83 % 0.07 s GPU @ 2.5 Ghz (Python)
89 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.
90 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.
91 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.
92 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.
93 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.
94 SegNet 88.59 % 83.54 % 88.35 % 88.84 % 12.88 % 11.16 % 0.01 s 8 cores @ 2.5 Ghz (C/C++)
95 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.
96 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.
97 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.
98 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.
99 Strait 86.36 % 91.98 % 85.75 % 86.97 % 15.89 % 13.03 % 69 ms GPU @ K20
100 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.
101 LWD 86.14 % 88.31 % 86.99 % 85.31 % 14.03 % 14.69 % 0.07 s GPU @ 2.5 Ghz (Python)
102 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++)
103 CNN_LSTM 84.98 % 83.43 % 90.34 % 80.22 % 9.43 % 19.78 % 54 ms GPU @ 2.0 Ghz (Python + C/C++)
104 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.
105 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.
106 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.
107 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.
108 LKW 75.48 % 78.73 % 65.97 % 88.18 % 50.00 % 11.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
109 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
This method makes use of Velodyne laser scans.
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
This method makes use of Velodyne laser scans.
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 RockyNet 95.08 % 85.88 % 92.01 % 98.36 % 2.78 % 1.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
11 ZongNet 95.08 % 85.88 % 92.01 % 98.36 % 2.78 % 1.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
12 BIRD
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++)
13 RSNet 94.84 % 91.65 % 94.56 % 95.13 % 1.78 % 4.87 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
14 ZmNet 94.72 % 85.71 % 91.82 % 97.82 % 2.84 % 2.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
15 KRS 94.60 % 92.83 % 94.96 % 94.25 % 1.63 % 5.75 % 1 s GPU @ 2.5 Ghz (Python)
16 LidCamNet
This method makes use of Velodyne laser scans.
94.54 % 92.74 % 94.64 % 94.45 % 1.74 % 5.55 % 0.15 s GPU @ 2.5 Ghz (Python)
17 WSLGAN 94.54 % 87.70 % 94.01 % 95.09 % 1.97 % 4.91 % 800ms GPU @ 1.5 Ghz (Python)
18 lkl_net 94.41 % 84.70 % 90.71 % 98.43 % 3.28 % 1.57 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
19 SSLGAN 94.40 % 87.84 % 94.17 % 94.63 % 1.91 % 5.37 % 700ms GPU @ 1.5 Ghz (Python)
X. Han, J. Lu, C. Zhao, S. You and H. Li: Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks. IEEE Signal Processing Letters 2018.
20 baseline 94.14 % 92.15 % 93.69 % 94.60 % 2.08 % 5.40 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
21 SeResNext+densefcn 94.09 % 84.58 % 90.58 % 97.89 % 3.32 % 2.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
22 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++)
23 RSNet2 93.98 % 91.88 % 93.81 % 94.15 % 2.03 % 5.85 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
24 RSNet- 93.90 % 91.69 % 92.94 % 94.88 % 2.35 % 5.12 % 0.07 s GPU @ 2.5 Ghz (Python)
25 SPN 93.89 % 88.19 % 94.56 % 93.23 % 1.75 % 6.77 % 1 s 1 core @ 2.5 Ghz (C/C++)
26 MMN 93.87 % 91.23 % 93.26 % 94.48 % 2.22 % 5.52 % 0.1 s GPU @ 2.5 Ghz (C/C++)
27 CoDNN 93.81 % 92.41 % 94.20 % 93.43 % 1.88 % 6.57 % 0.01 s 1 core @ 2.5 Ghz (Python + C/C++)
28 FNETMS 93.71 % 91.79 % 94.08 % 93.34 % 1.91 % 6.66 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
29 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.
30 THISV-005 93.67 % 92.63 % 95.65 % 91.76 % 1.36 % 8.24 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
31 THISV-004 93.66 % 92.64 % 95.66 % 91.75 % 1.36 % 8.25 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
32 IDA-Fusion
This method makes use of Velodyne laser scans.
93.63 % 86.07 % 92.22 % 95.08 % 2.61 % 4.92 % 0.1 s 4 cores @ 3.5 Ghz (C/C++)
33 THISV-003 93.63 % 92.63 % 95.24 % 92.07 % 1.50 % 7.93 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
34 wt 93.58 % 92.14 % 93.34 % 93.83 % 2.18 % 6.17 % 0.1 s GPU @ 1.0 Ghz (Python)
35 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.
36 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.
37 MuNet 93.18 % 86.13 % 92.03 % 94.36 % 2.66 % 5.64 % 0..8 s 1 core @ 2.5 Ghz (C/C++)
38 FDN 93.10 % 92.24 % 93.44 % 92.77 % 2.12 % 7.23 % 0.2 s GPU 1 core @ 2.5 Ghz (Python)
39 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++)
40 ChipNet
This method makes use of Velodyne laser scans.
92.91 % 84.95 % 90.98 % 94.91 % 3.06 % 5.09 % 12 ms GPU @ 1.5 Ghz (Keras)
41 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++)
42 MBN 92.89 % 83.53 % 89.43 % 96.63 % 3.72 % 3.37 % 0.16 s GPU @ 2.5 Ghz (Python)
43 UView 92.70 % 84.20 % 90.17 % 95.39 % 3.39 % 4.61 % 0.2 s GPU @ 1.0 Ghz (Python)
44 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.
45 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)
46 TDCac1 CNN 92.30 % 86.21 % 92.37 % 92.23 % 2.48 % 7.77 % .093 s 1 core @ 1.0 Ghz (C/C++)
47 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.
48 FCN-GCBs 92.10 % 83.69 % 89.61 % 94.73 % 3.58 % 5.27 % 0.08 s GPU @ 2.5 Ghz (C/C++)
49 FCN_RGBD
This method uses stereo information.
91.99 % 92.09 % 92.84 % 91.16 % 2.29 % 8.84 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
50 RDSN 91.98 % 85.64 % 91.45 % 92.53 % 2.82 % 7.47 % 0.25 s GPU @ 2.5 Ghz (Python)
51 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.
52 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.
53 RSNetVGG 91.72 % 91.52 % 92.62 % 90.84 % 2.36 % 9.16 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
54 DFN 91.70 % 87.72 % 91.51 % 91.89 % 2.78 % 8.11 % 0.25 s GPU @ >3.5 Ghz (Python)
55 THISV-002 91.46 % 91.90 % 92.18 % 90.74 % 2.51 % 9.26 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
56 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.
57 SUNet 91.10 % 81.62 % 87.32 % 95.22 % 4.50 % 4.78 % 0.018s
58 test-001 90.91 % 91.69 % 90.63 % 91.18 % 3.07 % 8.82 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
59 tFSSNet 90.59 % 80.50 % 86.09 % 95.58 % 5.03 % 4.42 % 0.02 s 1 core @ 2.5 Ghz (Python)
60 RD 90.03 % 78.89 % 90.96 % 89.11 % 2.89 % 10.89 % 4 s 1 core @ 3.0 Ghz (Matlab + C/C++)
61 HID-LS
This method makes use of Velodyne laser scans.
89.81 % 82.33 % 88.11 % 91.58 % 4.03 % 8.42 % 0.25 s 4 cores @ 3.0 Ghz (C/C++)
62 IINM 89.67 % 80.61 % 86.22 % 93.41 % 4.87 % 6.59 % 0.11 s 4 cores @ 3.0 Ghz (C/C++)
63 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.
64 HFM 89.20 % 80.48 % 86.07 % 92.56 % 4.88 % 7.44 % 5 s 2 cores @ 2.0 Ghz (C/C++)
65 fcn_rgbd 89.16 % 91.48 % 90.38 % 87.97 % 3.05 % 12.03 % 0.6 s 1 core @ 2.5 Ghz (C/C++)
66 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 2018.
67 SaimFCN 88.02 % 75.58 % 86.91 % 89.16 % 4.37 % 10.84 % tba s 1 core @ 2.5 Ghz (C/C++)
68 TFSeg 87.42 % 88.58 % 87.28 % 87.56 % 4.16 % 12.44 % 0.07 s GPU @ 1.0 Ghz (Python)
69 MCSL 87.16 % 78.96 % 84.40 % 90.10 % 5.43 % 9.90 % 0.106 s 4 cores @ 3.2 Ghz (C/C++)
70 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.
71 CNN-FCRF 86.33 % 72.39 % 83.02 % 89.92 % 5.99 % 10.08 % 1 s 4 cores @ 3.5 Ghz (C/C++)
72 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.
73 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.
74 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.
75 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.
76 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++)
X. Han, H. Wang, J. Lu and C. Zhao: Road detection based on the fusion of Lidar and image data. 2017.
77 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.
78 DVFCN 85.37 % 89.13 % 85.44 % 85.30 % 4.74 % 14.70 % 0.07 s GPU @ 2.5 Ghz (Python)
79 PAGM code 85.34 % 74.04 % 85.03 % 85.66 % 4.92 % 14.34 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
80 CNN_LSTM 85.15 % 76.59 % 88.15 % 82.36 % 3.61 % 17.64 % 20 ms GPU @ GTX950M (Python +Tensorflow)
81 LWD 84.96 % 87.51 % 82.18 % 87.93 % 6.21 % 12.07 % 0.07 s GPU @ 2.5 Ghz (Python)
82 LWDS 84.50 % 83.24 % 87.46 % 81.72 % 3.82 % 18.28 % 0.07 s GPU @ 2.5 Ghz (Python)
83 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.
84 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.
85 RoadNet-v2 84.33 % 87.33 % 82.12 % 86.66 % 6.15 % 13.34 % 10 GPU(GTX950M) @ 0.9 Ghz (Keras+Tensorflo)
86 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.
87 ResAXN 83.68 % 87.44 % 84.04 % 83.33 % 5.16 % 16.67 % 0.06 s GPU @ 1.5 Ghz (Python)
88 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.
89 PAGM_t code 83.55 % 70.28 % 80.44 % 86.90 % 6.88 % 13.10 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
90 CNN+BiGRU 82.91 % 71.28 % 88.79 % 77.76 % 3.20 % 22.24 % 20 ms GPU @ GTX950M (Python +Tensorflow)
91 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.
92 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.
93 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.
94 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.
95 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.
96 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++)
97 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.
98 Strait 79.75 % 84.94 % 78.52 % 81.02 % 7.22 % 18.98 % 69 ms GPU @ K20
99 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.
100 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++)
101 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.
102 SegNet 77.23 % 69.23 % 82.29 % 72.76 % 5.10 % 27.24 % 0.01 s 8 cores @ 2.5 Ghz (C/C++)
103 CNN_LSTM 76.28 % 65.25 % 80.51 % 72.47 % 5.72 % 27.53 % 54 ms GPU @ 2.0 Ghz (Python + C/C++)
104 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.
105 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.
106 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.
107 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.
108 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.
109 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.
110 LKW 69.65 % 74.02 % 65.45 % 74.42 % 12.80 % 25.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
111 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.
112 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 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
This method makes use of Velodyne laser scans.
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
This method makes use of Velodyne laser scans.
96.70 % 89.93 % 95.37 % 98.07 % 2.62 % 1.93 % .006 s GPU @ 3.5 Ghz (Python)
5 ZongNet 96.68 % 90.05 % 95.50 % 97.88 % 2.54 % 2.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
6 RockyNet 96.66 % 90.04 % 95.49 % 97.87 % 2.55 % 2.13 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
7 KRSF 96.50 % 94.01 % 96.74 % 96.27 % 1.78 % 3.73 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
8 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.
9 KRS 96.41 % 93.96 % 96.43 % 96.38 % 1.96 % 3.62 % 0.3 s GPU @ 2.5 Ghz (Python)
10 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.
11 ZmNet 96.21 % 89.74 % 95.16 % 97.28 % 2.73 % 2.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
12 LidCamNet
This method makes use of Velodyne laser scans.
96.03 % 93.93 % 96.23 % 95.83 % 2.07 % 4.17 % 0.15 s GPU @ 2.5 Ghz (Python)
13 lkl_net 95.99 % 88.66 % 93.98 % 98.09 % 3.46 % 1.91 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
14 BIRD
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++)
15 RSNet 95.86 % 93.21 % 95.68 % 96.05 % 2.39 % 3.95 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
16 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++)
17 WNet 95.82 % 93.31 % 95.88 % 95.76 % 2.26 % 4.24 % 0.1 s 4 cores @ 2.5 Ghz (Python)
18 WSLGAN 95.70 % 90.17 % 95.64 % 95.77 % 2.40 % 4.23 % 800ms GPU @ 1.5 Ghz (Python)
19 KRS 95.64 % 93.89 % 95.79 % 95.48 % 2.31 % 4.52 % 1 s GPU @ 2.5 Ghz (Python)
20 SeResNext+densefcn 95.60 % 88.50 % 93.79 % 97.49 % 3.55 % 2.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
21 baseline 95.58 % 93.51 % 95.19 % 95.97 % 2.67 % 4.03 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
22 SSLGAN 95.53 % 90.35 % 95.84 % 95.24 % 2.28 % 4.76 % 700ms GPU @ 1.5 Ghz (Python)
X. Han, J. Lu, C. Zhao, S. You and H. Li: Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks. IEEE Signal Processing Letters 2018.
23 RSNet2 95.35 % 93.32 % 95.20 % 95.49 % 2.65 % 4.51 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
24 RSNet- 95.29 % 93.37 % 94.96 % 95.62 % 2.80 % 4.38 % 0.07 s GPU @ 2.5 Ghz (Python)
25 SPN 95.29 % 90.44 % 95.93 % 94.65 % 2.21 % 5.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
26 IDA-Fusion
This method makes use of Velodyne laser scans.
95.22 % 89.31 % 94.69 % 95.76 % 2.96 % 4.24 % 0.1 s 4 cores @ 3.5 Ghz (C/C++)
27 MMN 95.12 % 92.99 % 94.82 % 95.42 % 2.87 % 4.58 % 0.1 s GPU @ 2.5 Ghz (C/C++)
28 CoDNN 95.06 % 93.58 % 95.24 % 94.88 % 2.61 % 5.12 % 0.01 s 1 core @ 2.5 Ghz (Python + C/C++)
29 FNETMS 94.99 % 93.18 % 94.90 % 95.09 % 2.82 % 4.91 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
30 MuNet 94.98 % 89.41 % 94.60 % 95.36 % 3.00 % 4.64 % 0..8 s 1 core @ 2.5 Ghz (C/C++)
31 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.
32 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.
33 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.
34 TDCac1 CNN 94.81 % 89.82 % 95.25 % 94.38 % 2.59 % 5.62 % .093 s 1 core @ 1.0 Ghz (C/C++)
35 THISV-005 94.76 % 93.78 % 96.50 % 93.07 % 1.86 % 6.93 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
36 FDN 94.75 % 93.63 % 94.65 % 94.86 % 2.96 % 5.14 % 0.2 s GPU 1 core @ 2.5 Ghz (Python)
37 THISV-004 94.69 % 93.77 % 96.39 % 93.04 % 1.92 % 6.96 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
38 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++)
39 wt 94.63 % 93.56 % 94.38 % 94.88 % 3.11 % 5.12 % 0.1 s GPU @ 1.0 Ghz (Python)
40 MBN 94.63 % 87.37 % 92.55 % 96.80 % 4.29 % 3.20 % 0.16 s GPU @ 2.5 Ghz (Python)
41 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++)
42 THISV-003 94.36 % 93.78 % 96.00 % 92.78 % 2.13 % 7.22 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
43 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)
44 UView 94.23 % 87.98 % 93.23 % 95.24 % 3.81 % 4.76 % 0.2 s GPU @ 1.0 Ghz (Python)
45 FCN_RGBD
This method uses stereo information.
94.14 % 93.74 % 94.81 % 93.48 % 2.82 % 6.52 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
46 FCN-GCBs 94.08 % 87.66 % 92.87 % 95.32 % 4.03 % 4.68 % 0.08 s GPU @ 2.5 Ghz (C/C++)
47 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.
48 ChipNet
This method makes use of Velodyne laser scans.
94.05 % 88.29 % 93.57 % 94.53 % 3.58 % 5.47 % 12 ms GPU @ 1.5 Ghz (Keras)
49 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.
50 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.
51 RDSN 93.75 % 88.33 % 93.55 % 93.96 % 3.57 % 6.04 % 0.25 s GPU @ 2.5 Ghz (Python)
52 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.
53 THISV-002 93.39 % 93.51 % 93.69 % 93.09 % 3.46 % 6.91 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
54 SUNet 93.38 % 87.18 % 92.35 % 94.44 % 4.31 % 5.56 % 0.018s
55 RSNetVGG 93.24 % 93.10 % 94.68 % 91.84 % 2.85 % 8.16 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
56 DFN 93.23 % 89.59 % 93.11 % 93.35 % 3.81 % 6.65 % 0.25 s GPU @ >3.5 Ghz (Python)
57 HID-LS
This method makes use of Velodyne laser scans.
93.11 % 87.33 % 92.52 % 93.71 % 4.18 % 6.29 % 0.25 s 4 cores @ 3.0 Ghz (C/C++)
58 test-001 93.11 % 93.41 % 93.41 % 92.81 % 3.61 % 7.19 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
59 tFSSNet 93.03 % 85.63 % 90.64 % 95.54 % 5.43 % 4.46 % 0.02 s 1 core @ 2.5 Ghz (Python)
60 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.
61 fcn_rgbd 92.25 % 93.15 % 91.62 % 92.88 % 4.68 % 7.12 % 0.6 s 1 core @ 2.5 Ghz (C/C++)
62 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.
63 SaimFCN 91.51 % 85.79 % 90.82 % 92.21 % 5.13 % 7.79 % tba s 1 core @ 2.5 Ghz (C/C++)
64 CNN-FCRF 91.40 % 84.22 % 89.09 % 93.84 % 6.33 % 6.16 % 1 s 4 cores @ 3.5 Ghz (C/C++)
65 HFM 90.88 % 83.10 % 87.86 % 94.12 % 7.16 % 5.88 % 5 s 2 cores @ 2.0 Ghz (C/C++)
66 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 2018.
67 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.
68 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.
69 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.
70 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++)
X. Han, H. Wang, J. Lu and C. Zhao: Road detection based on the fusion of Lidar and image data. 2017.
71 PAGM code 90.08 % 80.52 % 90.53 % 89.64 % 5.17 % 10.36 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
72 LWDS 89.83 % 87.04 % 91.61 % 88.12 % 4.45 % 11.88 % 0.07 s GPU @ 2.5 Ghz (Python)
73 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.
74 TFSeg 89.65 % 89.24 % 88.79 % 90.52 % 6.30 % 9.48 % 0.07 s GPU @ 1.0 Ghz (Python)
75 ResAXN 89.39 % 91.91 % 90.84 % 87.98 % 4.89 % 12.02 % 0.06 s GPU @ 1.5 Ghz (Python)
76 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.
77 RoadNet-v2 89.08 % 91.60 % 88.12 % 90.06 % 6.69 % 9.94 % 10 GPU(GTX950M) @ 0.9 Ghz (Keras+Tensorflo)
78 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.
79 DVFCN 88.34 % 91.70 % 88.51 % 88.17 % 6.30 % 11.83 % 0.07 s GPU @ 2.5 Ghz (Python)
80 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.
81 CNN_LSTM 88.23 % 81.09 % 91.22 % 85.43 % 4.53 % 14.57 % 20 ms GPU @ GTX950M (Python +Tensorflow)
82 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.
83 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.
84 PAGM_t code 87.32 % 77.28 % 86.56 % 88.09 % 7.53 % 11.91 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
85 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.
86 CNN+BiGRU 86.91 % 81.11 % 91.24 % 82.97 % 4.39 % 17.03 % 20 ms GPU @ GTX950M (Python +Tensorflow)
87 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.
88 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.
89 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++)
90 LWD 85.74 % 84.86 % 83.76 % 87.81 % 9.38 % 12.19 % 0.07 s GPU @ 2.5 Ghz (Python)
91 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.
92 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.
93 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.
94 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.
95 SegNet 84.04 % 78.76 % 85.50 % 82.63 % 7.72 % 17.37 % 0.01 s 8 cores @ 2.5 Ghz (C/C++)
96 Strait 84.03 % 88.71 % 82.95 % 85.13 % 9.64 % 14.87 % 69 ms GPU @ K20
97 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.
98 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.
99 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.
100 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++)
101 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.
102 CNN_LSTM 80.91 % 72.11 % 85.84 % 76.52 % 6.96 % 23.48 % 54 ms GPU @ 2.0 Ghz (Python + C/C++)
103 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.
104 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.
105 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.
106 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.
107 LKW 75.53 % 79.80 % 69.68 % 82.44 % 19.75 % 17.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
108 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.
109 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++)
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
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 NVLaneNet 91.86 % 91.42 % 90.89 % 92.85 % 1.64 % 7.15 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
5 ILN 91.62 % 91.17 % 91.98 % 91.26 % 1.40 % 8.74 % 0.24 s 1 core @ GPU (Python)
6 FCN-GCBs 91.24 % 85.14 % 92.15 % 90.35 % 1.35 % 9.65 % 0.08 s GPU @ 2.5 Ghz (C/C++)
7 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.
8 RD 90.01 % 81.60 % 88.26 % 91.82 % 2.15 % 8.18 % 4 s 1 core @ 3.0 Ghz (Matlab + C/C++)
9 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.
10 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.
11 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.
12 LKW 75.85 % 79.27 % 71.49 % 80.77 % 5.67 % 19.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
13 Strait 70.82 % 74.07 % 65.20 % 77.50 % 7.28 % 22.50 % 69 ms GPU @ K20
14 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.
15 test-001 67.77 % 55.50 % 55.94 % 85.96 % 11.92 % 14.04 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
16 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++)
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
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 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 ILN 99.15 % 99.10 % 98.77 % 99.42 % 97.42 % 95.21 % 96.74 % 83.91 % 73.68 % 0.24 s 1 core @ GPU (Python)
9 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.
10 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.
11 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.
12 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++)
13 test-001 96.85 % 94.72 % 91.98 % 92.48 % 86.68 % 80.79 % 76.67 % 50.59 % 42.67 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
14 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.
15 Strait 94.97 % 91.74 % 88.38 % 89.60 % 81.85 % 73.89 % 67.02 % 48.28 % 32.89 % 69 ms GPU @ K20
16 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}
}



eXTReMe Tracker