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!

Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.
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 DH-OCR 96.84 % 91.10 % 97.08 % 96.60 % 1.32 % 3.40 % 0.2 s GPU @ 2.5 Ghz (C/C++)
2 ZongNet 96.70 % 90.12 % 96.00 % 97.41 % 1.85 % 2.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 RockyNet 96.65 % 90.09 % 95.97 % 97.35 % 1.86 % 2.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 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)
5 lkl_net 96.04 % 89.10 % 94.88 % 97.22 % 2.39 % 2.78 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
6 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)
L. Caltagirone, M. Bellone, L. Svensson and M. Wahde: LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks. CoRR 2018.
7 ZmNet 95.49 % 89.72 % 95.56 % 95.42 % 2.02 % 4.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
8 SeResNext+densefcn 95.04 % 88.82 % 94.57 % 95.51 % 2.50 % 4.49 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
9 LC-CRF
This method makes use of Velodyne laser scans.
94.91 % 86.41 % 91.92 % 98.11 % 3.93 % 1.89 % 0.18 s GPU @ 1.5 Ghz (Python + C/C++)
10 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.
11 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.
12 MuNet 94.57 % 88.61 % 94.24 % 94.90 % 2.64 % 5.10 % 0..8 s 1 core @ 2.5 Ghz (C/C++)
13 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.
14 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.
15 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.
16 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)
17 FCN-RGBD
This method uses stereo information.
93.78 % 93.40 % 94.71 % 92.86 % 2.36 % 7.14 % 0.21 s 1 core @ 2.5 Ghz (Python)
18 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)
19 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.
20 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++)
21 THISV-004 93.32 % 93.20 % 96.46 % 90.38 % 1.51 % 9.62 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
22 MFF 93.31 % 91.04 % 93.55 % 93.07 % 2.92 % 6.93 % 0.1 s TITANX @ 2.5 Ghz (TensorFlow)
23 UView 93.15 % 86.18 % 91.67 % 94.67 % 3.92 % 5.33 % 0.2 s GPU @ 1.0 Ghz (Python)
24 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 1 cores @ 3.0 Ghz (C/C++)
S. Gu, Y. Zhang, J. Yang and H. Kong: Lidar-based urban road detection by histograms of normalized inverse depths and line scanning. ECMR 2017.
S. Gu, Y. Zhang, X. Yuan, J. Yang, T. Wu and H. Kong: Histograms of the Normalized Inverse Depth and Line Scanning for Urban Road Detection. IEEE Trans. Intelligent Transportation Systems .
25 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.
26 THISV-003 92.69 % 92.76 % 96.11 % 89.51 % 1.65 % 10.49 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
27 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.
28 OFA 92.08 % 82.73 % 87.87 % 96.72 % 6.08 % 3.28 % 0.04 s GPU @ 1.5 Ghz (Python)
29 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.
30 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.
31 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++)
32 test-001 91.09 % 92.63 % 92.56 % 89.67 % 3.28 % 10.33 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
33 SaimFCN 91.03 % 84.64 % 89.98 % 92.11 % 4.67 % 7.89 % tba s 1 core @ 2.5 Ghz (C/C++)
34 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.
35 tFSSNet 90.83 % 83.99 % 89.26 % 92.46 % 5.07 % 7.54 % 0.02 s 1 core @ 2.5 Ghz (Python)
36 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.
37 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.
38 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.
39 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.
40 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.
41 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.
42 PAGM code 89.08 % 78.11 % 88.52 % 89.66 % 5.30 % 10.34 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
43 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.
44 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.
45 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.
46 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.
47 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.
48 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++)
49 multi-task CNN 85.95 % 81.28 % 77.40 % 96.64 % 12.86 % 3.36 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
50 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.
51 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.
52 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.
53 CNN+BiGRU 85.21 % 79.80 % 90.57 % 80.44 % 3.82 % 19.56 % 20 ms GPU @ GTX950M (Python +Tensorflow)
54 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.
55 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.
56 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.
57 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.
58 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.
59 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.
60 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.
61 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.
62 CNN_LSTM 78.42 % 69.11 % 83.28 % 74.09 % 6.78 % 25.91 % 54 ms GPU @ 2.0 Ghz (Python + C/C++)
63 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.
64 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.
65 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.
66 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.
67 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.
68 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.
69 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 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)
2 RockyNet 97.53 % 93.05 % 97.12 % 97.94 % 3.19 % 2.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 ZongNet 97.53 % 93.05 % 97.12 % 97.94 % 3.19 % 2.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 ZmNet 97.46 % 92.71 % 96.75 % 98.19 % 3.63 % 1.81 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
5 DH-OCR 97.13 % 93.45 % 97.56 % 96.72 % 2.66 % 3.28 % 0.2 s GPU @ 2.5 Ghz (C/C++)
6 LC-CRF
This method makes use of Velodyne laser scans.
97.08 % 92.06 % 96.03 % 98.16 % 4.46 % 1.84 % 0.18 s GPU @ 1.5 Ghz (Python + C/C++)
7 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)
L. Caltagirone, M. Bellone, L. Svensson and M. Wahde: LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks. CoRR 2018.
8 lkl_net 96.82 % 91.31 % 95.21 % 98.48 % 5.45 % 1.52 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
9 SeResNext+densefcn 96.78 % 91.19 % 95.07 % 98.56 % 5.62 % 1.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
10 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.
11 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.
12 MuNet 96.20 % 92.44 % 96.31 % 96.09 % 4.05 % 3.91 % 0..8 s 1 core @ 2.5 Ghz (C/C++)
13 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.
14 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++)
15 THISV-004 96.10 % 95.45 % 96.47 % 95.73 % 3.85 % 4.27 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
16 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.
17 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.
18 MFF 95.92 % 93.79 % 95.25 % 96.59 % 5.29 % 3.41 % 0.1 s TITANX @ 2.5 Ghz (TensorFlow)
19 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++)
20 THISV-003 95.85 % 95.49 % 96.74 % 94.98 % 3.52 % 5.02 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
21 UView 95.78 % 92.02 % 95.26 % 96.31 % 5.26 % 3.69 % 0.2 s GPU @ 1.0 Ghz (Python)
22 tFSSNet 95.78 % 90.32 % 94.11 % 97.51 % 6.71 % 2.49 % 0.02 s 1 core @ 2.5 Ghz (Python)
23 FCN-RGBD
This method uses stereo information.
95.55 % 95.46 % 95.67 % 95.44 % 4.75 % 4.56 % 0.21 s 1 core @ 2.5 Ghz (Python)
24 test-001 95.55 % 95.33 % 95.45 % 95.65 % 5.01 % 4.35 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
25 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.
26 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.
27 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)
28 OFA 95.43 % 89.10 % 92.78 % 98.24 % 8.41 % 1.76 % 0.04 s GPU @ 1.5 Ghz (Python)
29 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 1 cores @ 3.0 Ghz (C/C++)
S. Gu, Y. Zhang, J. Yang and H. Kong: Lidar-based urban road detection by histograms of normalized inverse depths and line scanning. ECMR 2017.
S. Gu, Y. Zhang, X. Yuan, J. Yang, T. Wu and H. Kong: Histograms of the Normalized Inverse Depth and Line Scanning for Urban Road Detection. IEEE Trans. Intelligent Transportation Systems .
30 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)
31 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.
32 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.
33 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.
34 SaimFCN 93.68 % 89.74 % 93.48 % 93.87 % 7.20 % 6.13 % tba s 1 core @ 2.5 Ghz (C/C++)
35 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.
36 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.
37 PAGM code 93.29 % 91.04 % 94.91 % 91.72 % 5.41 % 8.28 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
38 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.
39 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.
40 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.
41 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.
42 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.
43 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.
44 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.
45 multi-task CNN 91.15 % 87.45 % 85.08 % 98.15 % 18.92 % 1.85 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
46 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.
47 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.
48 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.
49 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++)
50 CNN+BiGRU 90.02 % 85.49 % 92.84 % 87.36 % 7.40 % 12.64 % 20 ms GPU @ GTX950M (Python +Tensorflow)
51 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.
52 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.
53 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.
54 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.
55 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.
56 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.
57 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.
58 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.
59 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.
60 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.
61 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.
62 CNN_LSTM 84.98 % 83.43 % 90.34 % 80.22 % 9.43 % 19.78 % 54 ms GPU @ 2.0 Ghz (Python + C/C++)
63 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.
64 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.
65 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.
66 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.
67 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 DH-OCR 95.47 % 89.18 % 95.63 % 95.31 % 1.42 % 4.69 % 0.2 s GPU @ 2.5 Ghz (C/C++)
2 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)
3 RockyNet 95.08 % 85.88 % 92.01 % 98.36 % 2.78 % 1.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 ZongNet 95.08 % 85.88 % 92.01 % 98.36 % 2.78 % 1.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
5 ZmNet 94.72 % 85.71 % 91.82 % 97.82 % 2.84 % 2.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
6 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)
L. Caltagirone, M. Bellone, L. Svensson and M. Wahde: LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks. CoRR 2018.
7 lkl_net 94.41 % 84.70 % 90.71 % 98.43 % 3.28 % 1.57 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
8 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.
9 SeResNext+densefcn 94.09 % 84.58 % 90.58 % 97.89 % 3.32 % 2.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
10 LC-CRF
This method makes use of Velodyne laser scans.
94.01 % 85.24 % 91.31 % 96.88 % 3.00 % 3.12 % 0.18 s GPU @ 1.5 Ghz (Python + C/C++)
11 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.
12 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++)
13 THISV-004 93.66 % 92.64 % 95.66 % 91.75 % 1.36 % 8.25 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
14 THISV-003 93.63 % 92.63 % 95.24 % 92.07 % 1.50 % 7.93 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
15 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.
16 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.
17 MuNet 93.18 % 86.13 % 92.03 % 94.36 % 2.66 % 5.64 % 0..8 s 1 core @ 2.5 Ghz (C/C++)
18 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)
19 UView 92.70 % 84.20 % 90.17 % 95.39 % 3.39 % 4.61 % 0.2 s GPU @ 1.0 Ghz (Python)
20 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.
21 OFA 92.62 % 83.12 % 88.97 % 96.58 % 3.90 % 3.42 % 0.04 s GPU @ 1.5 Ghz (Python)
22 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)
23 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.
24 FCN-RGBD
This method uses stereo information.
91.99 % 92.09 % 92.84 % 91.16 % 2.29 % 8.84 % 0.21 s 1 core @ 2.5 Ghz (Python)
25 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.
26 MFF 91.83 % 89.52 % 91.60 % 92.07 % 2.75 % 7.93 % 0.1 s TITANX @ 2.5 Ghz (TensorFlow)
27 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.
28 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++)
29 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.
30 test-001 90.91 % 91.69 % 90.63 % 91.18 % 3.07 % 8.82 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
31 tFSSNet 90.59 % 80.50 % 86.09 % 95.58 % 5.03 % 4.42 % 0.02 s 1 core @ 2.5 Ghz (Python)
32 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 1 cores @ 3.0 Ghz (C/C++)
S. Gu, Y. Zhang, J. Yang and H. Kong: Lidar-based urban road detection by histograms of normalized inverse depths and line scanning. ECMR 2017.
S. Gu, Y. Zhang, X. Yuan, J. Yang, T. Wu and H. Kong: Histograms of the Normalized Inverse Depth and Line Scanning for Urban Road Detection. IEEE Trans. Intelligent Transportation Systems .
33 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.
34 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.
35 SaimFCN 88.02 % 75.58 % 86.91 % 89.16 % 4.37 % 10.84 % tba s 1 core @ 2.5 Ghz (C/C++)
36 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.
37 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.
38 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.
39 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.
40 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.
41 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.
42 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.
43 PAGM code 85.34 % 74.04 % 85.03 % 85.66 % 4.92 % 14.34 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
44 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.
45 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.
46 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.
47 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.
48 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++)
49 CNN+BiGRU 82.91 % 71.28 % 88.79 % 77.76 % 3.20 % 22.24 % 20 ms GPU @ GTX950M (Python +Tensorflow)
50 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.
51 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.
52 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.
53 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.
54 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.
55 multi-task CNN 80.45 % 75.87 % 68.63 % 97.19 % 14.48 % 2.81 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
56 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.
57 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.
58 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.
59 CNN_LSTM 76.28 % 65.25 % 80.51 % 72.47 % 5.72 % 27.53 % 54 ms GPU @ 2.0 Ghz (Python + C/C++)
60 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.
61 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.
62 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.
63 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.
64 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.
65 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.
66 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.
67 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 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)
2 ZongNet 96.68 % 90.05 % 95.50 % 97.88 % 2.54 % 2.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 RockyNet 96.66 % 90.04 % 95.49 % 97.87 % 2.55 % 2.13 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 DH-OCR 96.64 % 91.36 % 96.95 % 96.34 % 1.67 % 3.66 % 0.2 s GPU @ 2.5 Ghz (C/C++)
5 ZmNet 96.21 % 89.74 % 95.16 % 97.28 % 2.73 % 2.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
6 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)
L. Caltagirone, M. Bellone, L. Svensson and M. Wahde: LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks. CoRR 2018.
7 lkl_net 95.99 % 88.66 % 93.98 % 98.09 % 3.46 % 1.91 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
8 LC-CRF
This method makes use of Velodyne laser scans.
95.68 % 88.34 % 93.62 % 97.83 % 3.67 % 2.17 % 0.18 s GPU @ 1.5 Ghz (Python + C/C++)
9 SeResNext+densefcn 95.60 % 88.50 % 93.79 % 97.49 % 3.55 % 2.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
10 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.
11 MuNet 94.98 % 89.41 % 94.60 % 95.36 % 3.00 % 4.64 % 0..8 s 1 core @ 2.5 Ghz (C/C++)
12 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.
13 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.
14 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.
15 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++)
16 THISV-004 94.69 % 93.77 % 96.39 % 93.04 % 1.92 % 6.96 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
17 THISV-003 94.36 % 93.78 % 96.00 % 92.78 % 2.13 % 7.22 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
18 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)
19 UView 94.23 % 87.98 % 93.23 % 95.24 % 3.81 % 4.76 % 0.2 s GPU @ 1.0 Ghz (Python)
20 FCN-RGBD
This method uses stereo information.
94.14 % 93.74 % 94.81 % 93.48 % 2.82 % 6.52 % 0.21 s 1 core @ 2.5 Ghz (Python)
21 MFF 94.14 % 91.64 % 93.81 % 94.46 % 3.43 % 5.54 % 0.1 s TITANX @ 2.5 Ghz (TensorFlow)
22 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.
23 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)
24 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.
25 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.
26 OFA 93.74 % 85.37 % 90.36 % 97.38 % 5.72 % 2.62 % 0.04 s GPU @ 1.5 Ghz (Python)
27 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.
28 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++)
29 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 1 cores @ 3.0 Ghz (C/C++)
S. Gu, Y. Zhang, J. Yang and H. Kong: Lidar-based urban road detection by histograms of normalized inverse depths and line scanning. ECMR 2017.
S. Gu, Y. Zhang, X. Yuan, J. Yang, T. Wu and H. Kong: Histograms of the Normalized Inverse Depth and Line Scanning for Urban Road Detection. IEEE Trans. Intelligent Transportation Systems .
30 test-001 93.11 % 93.41 % 93.41 % 92.81 % 3.61 % 7.19 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
31 tFSSNet 93.03 % 85.63 % 90.64 % 95.54 % 5.43 % 4.46 % 0.02 s 1 core @ 2.5 Ghz (Python)
32 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.
33 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.
34 SaimFCN 91.51 % 85.79 % 90.82 % 92.21 % 5.13 % 7.79 % tba s 1 core @ 2.5 Ghz (C/C++)
35 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.
36 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.
37 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.
38 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.
39 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.
40 PAGM code 90.08 % 80.52 % 90.53 % 89.64 % 5.17 % 10.36 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
41 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.
42 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.
43 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.
44 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.
45 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.
46 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.
47 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++)
48 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.
49 CNN+BiGRU 86.91 % 81.11 % 91.24 % 82.97 % 4.39 % 17.03 % 20 ms GPU @ GTX950M (Python +Tensorflow)
50 multi-task CNN 86.81 % 82.15 % 78.26 % 97.47 % 14.92 % 2.53 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
51 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.
52 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.
53 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.
54 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.
55 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.
56 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.
57 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.
58 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.
59 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.
60 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.
61 CNN_LSTM 80.91 % 72.11 % 85.84 % 76.52 % 6.96 % 23.48 % 54 ms GPU @ 2.0 Ghz (Python + C/C++)
62 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.
63 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.
64 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.
65 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.
66 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.
67 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 NVLaneNet 91.86 % 91.42 % 90.89 % 92.85 % 1.64 % 7.15 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
2 ILN 91.62 % 91.17 % 91.98 % 91.26 % 1.40 % 8.74 % 0.24 s 1 core @ GPU (Python)
3 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.
4 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.
5 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.
6 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.
7 test-001 67.77 % 55.50 % 55.94 % 85.96 % 11.92 % 14.04 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
8 DH-OCR 63.60 % 44.14 % 47.05 % 98.08 % 19.43 % 1.92 % 0.2 s GPU @ 2.5 Ghz (C/C++)
9 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 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++)
2 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.
3 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)
4 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.
5 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.
6 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++)
7 DH-OCR 95.48 % 88.86 % 83.75 % 90.33 % 81.46 % 76.08 % 63.44 % 48.28 % 50.00 % 0.2 s GPU @ 2.5 Ghz (C/C++)
8 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.
9 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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