Road/Lane Detection Evaluation 2013


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

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

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

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

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

Road Estimation Evaluation

UM_ROAD


Method Setting Code MaxF AP PRE REC FPR FNR Runtime Environment
1 UNV 96.69 % 92.41 % 97.38 % 96.01 % 1.18 % 3.99 % 1.2 s GPU @ 3.0 Ghz (Python + C/C++)
2 RPP 96.04 % 89.77 % 95.61 % 96.48 % 2.02 % 3.52 % 0.16 s GPU (C/C++)
3 SAIT 95.65 % 90.17 % 96.06 % 95.25 % 1.78 % 4.75 % 0.04 s GPU @ 2.5 Ghz (C/C++)
4 TuSimple 95.64 % 93.50 % 95.46 % 95.83 % 2.08 % 4.17 % 0.2s GPU @ 2.5 Ghz (Python)
5 SAIT 95.09 % 89.58 % 95.41 % 94.76 % 2.08 % 5.24 % 0.04 s GPU @ 2.5 Ghz (C/C++)
6 TDCac1 CNN 94.86 % 89.62 % 95.45 % 94.28 % 2.05 % 5.72 % .093 s 1 core @ 1.0 Ghz (C/C++)
7 RPN 94.77 % 91.42 % 95.16 % 94.37 % 2.19 % 5.63 % 0.18 s GPU @ 2.5 Ghz (Matlab + C/C++)
8 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++)
9 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.
10 SSL 93.94 % 89.36 % 94.78 % 93.11 % 2.34 % 6.89 % 0.05 s GPU @ 2.5 Ghz (C/C++)
11 RDBN 93.90 % 91.23 % 94.13 % 93.67 % 2.66 % 6.33 % 0.25 s GPU @ 2.5 Ghz (Matlab + C/C++)
12 CoDNN 93.90 % 92.86 % 94.51 % 93.29 % 2.47 % 6.71 % 0.01 s 1 core @ 2.5 Ghz (Python + C/C++)
13 AXN 93.86 % 92.63 % 94.09 % 93.63 % 2.68 % 6.37 % 0.06 s GPU @ 2.5 Ghz (C/C++)
14 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++)
15 FCN-GCBs 93.86 % 86.62 % 92.15 % 95.62 % 3.71 % 4.38 % 0.08 s GPU @ 2.5 Ghz (C/C++)
16 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.
17 uickitti 93.27 % 92.92 % 93.20 % 93.34 % 3.10 % 6.66 % 0.1 s GPU @ 1.0 Ghz (Python)
18 s-FCN-loc 92.85 % 87.41 % 93.02 % 92.68 % 3.17 % 7.32 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
19 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.
20 Up-Conv-Poly 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.
21 FF 91.63 % 87.14 % 92.72 % 90.56 % 3.24 % 9.44 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
22 JOJnet 91.47 % 87.64 % 92.12 % 90.82 % 3.54 % 9.18 % 0.1 s GPU @ 2.5 Ghz (C/C++)
23 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.
24 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 and B. Dai: Hybrid Conditional Random Field based Camera-LIDAR Fusion for Road Detection. Information Sciences. in press.
25 SGL 90.94 % 86.35 % 91.86 % 90.05 % 3.64 % 9.95 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
26 VGGFCN 90.93 % 83.56 % 88.79 % 93.18 % 5.36 % 6.82 % 0.4 s GPU @ 1.0 Ghz (Python + C/C++)
27 MixedCRF
This method makes use of Velodyne laser scans.
90.83 % 83.84 % 89.09 % 92.64 % 5.17 % 7.36 % 6s 1 core @ 2.5 Ghz (Matlab + C/C++)
28 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.
29 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.
30 Feature++
This method uses stereo information.
90.43 % 88.83 % 89.63 % 91.26 % 4.81 % 8.74 % 13 s 4 core @ 2.5 Ghz (Matlab + C/C++)
31 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.
32 LidarHisto
This method makes use of Velodyne laser scans.
89.87 % 83.03 % 91.28 % 88.49 % 3.85 % 11.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
33 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.
34 LWDS 89.50 % 86.11 % 90.59 % 88.44 % 4.19 % 11.56 % 0.07 s GPU @ 2.5 Ghz (Python)
35 BNV
This method uses stereo information.
89.42 % 83.13 % 88.31 % 90.55 % 5.46 % 9.45 % 3 s 2 cores @ 2.5 Ghz (C/C++)
36 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.
37 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.
38 VPD
This method uses stereo information.
88.87 % 82.04 % 93.31 % 84.83 % 2.77 % 15.17 % 4 s 1 core @ 3.0 Ghz (Matlab + C/C++)
39 DVFCN 88.64 % 91.37 % 89.20 % 88.10 % 4.86 % 11.90 % 0.07 s GPU @ 2.5 Ghz (Python)
40 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.
41 FCNB 87.98 % 77.50 % 87.77 % 88.18 % 5.60 % 11.82 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
42 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.
43 LWD 87.39 % 88.85 % 84.99 % 89.92 % 7.24 % 10.08 % 0.07 s GPU @ 2.5 Ghz (Python)
44 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.
45 Multimodal 87.28 % 90.82 % 87.12 % 87.44 % 5.89 % 12.56 % 0.3 s 4 cores @ 2.5 Ghz (Matlab)
46 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.
47 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.
48 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.
49 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.
50 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++)
51 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.
52 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.
53 RNF 83.71 % 72.64 % 88.14 % 79.71 % 4.89 % 20.29 % .5 s 1 core @ 2.5 Ghz (C/C++)
54 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.
55 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.
56 PFH+HSV 82.99 % 74.81 % 84.48 % 81.56 % 6.83 % 18.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 FCNS 82.97 % 87.01 % 80.84 % 85.21 % 9.20 % 14.79 % 6 s 1 core @ 2.5 Ghz (C/C++)
58 INM 82.56 % 74.66 % 90.92 % 75.61 % 3.44 % 24.39 % 5 s 1 core @ 2.5 Ghz (C/C++)
59 SegNet 82.17 % 76.46 % 84.03 % 80.40 % 6.97 % 19.60 % 0.01 s 8 cores @ 2.5 Ghz (C/C++)
60 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++)
61 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.
62 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.
63 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.
64 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.
65 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.
66 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.
67 RFH 76.18 % 61.64 % 68.38 % 86.00 % 18.12 % 14.00 % .5 s 1 core @ 2.5 Ghz (C/C++)
68 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.
69 NSV 73.15 % 69.75 % 61.48 % 90.29 % 25.77 % 9.71 % 5 s GPU @ 3.5 Ghz (Matlab)
70 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.
71 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.
Table as LaTeX | Only published Methods

UMM_ROAD


Method Setting Code MaxF AP PRE REC FPR FNR Runtime Environment
1 TuSimple 97.62 % 95.53 % 97.41 % 97.82 % 2.86 % 2.18 % 0.2s GPU @ 2.5 Ghz (Python)
2 UNV 97.34 % 94.23 % 97.52 % 97.16 % 2.71 % 2.84 % 1.2 s GPU @ 3.0 Ghz (Python + C/C++)
3 SAIT 97.31 % 92.68 % 96.71 % 97.91 % 3.66 % 2.09 % 0.04 s GPU @ 2.5 Ghz (C/C++)
4 SAIT 97.15 % 93.34 % 97.44 % 96.86 % 2.79 % 3.14 % 0.04 s GPU @ 2.5 Ghz (C/C++)
5 RPP 97.03 % 92.36 % 96.36 % 97.70 % 4.06 % 2.30 % 0.16 s GPU (C/C++)
6 CoDNN 96.56 % 95.33 % 96.41 % 96.71 % 3.96 % 3.29 % 0.01 s 1 core @ 2.5 Ghz (Python + C/C++)
7 AXN 96.20 % 95.29 % 95.91 % 96.49 % 4.52 % 3.51 % 0.06 s GPU @ 2.5 Ghz (C/C++)
8 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.
9 uickitti 96.11 % 95.44 % 96.08 % 96.14 % 4.31 % 3.86 % 0.1 s GPU @ 1.0 Ghz (Python)
10 TDCac1 CNN 96.11 % 92.62 % 96.65 % 95.57 % 3.64 % 4.43 % .093 s 1 core @ 1.0 Ghz (C/C++)
11 RPN 96.06 % 93.49 % 95.80 % 96.31 % 4.64 % 3.69 % 0.18 s GPU @ 2.5 Ghz (Matlab + C/C++)
12 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.
13 SSL 96.01 % 93.25 % 96.43 % 95.59 % 3.89 % 4.41 % 0.05 s GPU @ 2.5 Ghz (C/C++)
14 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++)
15 FF 95.79 % 93.85 % 98.00 % 93.68 % 2.10 % 6.32 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
16 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++)
17 Up-Conv-Poly 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.
18 FCN-GCBs 95.29 % 91.27 % 95.16 % 95.43 % 5.33 % 4.57 % 0.08 s GPU @ 2.5 Ghz (C/C++)
19 RDBN 95.24 % 93.78 % 96.01 % 94.48 % 4.31 % 5.52 % 0.25 s GPU @ 2.5 Ghz (Matlab + C/C++)
20 s-FCN-loc 95.01 % 91.86 % 95.81 % 94.23 % 4.53 % 5.77 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
21 VGGFCN 94.26 % 91.14 % 95.02 % 93.51 % 5.38 % 6.49 % 0.4 s GPU @ 1.0 Ghz (Python + C/C++)
22 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.
23 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.
24 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.
25 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.
26 Feature++
This method uses stereo information.
93.55 % 92.34 % 92.77 % 94.34 % 8.08 % 5.66 % 13 s 4 core @ 2.5 Ghz (Matlab + C/C++)
27 JOJnet 93.42 % 88.88 % 91.93 % 94.96 % 9.16 % 5.04 % 0.1 s GPU @ 2.5 Ghz (C/C++)
28 LidarHisto
This method makes use of Velodyne laser scans.
93.32 % 93.19 % 95.39 % 91.34 % 4.85 % 8.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
29 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.
30 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.
31 LWDS 92.81 % 94.66 % 94.41 % 91.25 % 5.93 % 8.75 % 0.07 s GPU @ 2.5 Ghz (Python)
32 SGL 92.39 % 87.73 % 95.59 % 89.40 % 4.53 % 10.60 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
33 MixedCRF
This method makes use of Velodyne laser scans.
92.29 % 90.06 % 93.83 % 90.80 % 6.56 % 9.20 % 6s 1 core @ 2.5 Ghz (Matlab + C/C++)
34 BNV
This method uses stereo information.
92.21 % 87.99 % 91.55 % 92.89 % 9.43 % 7.11 % 3 s 2 cores @ 2.5 Ghz (C/C++)
35 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 and B. Dai: Hybrid Conditional Random Field based Camera-LIDAR Fusion for Road Detection. Information Sciences. in press.
36 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.
37 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.
38 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.
39 Multimodal 91.31 % 94.23 % 89.75 % 92.92 % 11.67 % 7.08 % 0.3 s 4 cores @ 2.5 Ghz (Matlab)
40 FCNS 91.28 % 93.68 % 90.51 % 92.07 % 10.61 % 7.93 % 6 s 1 core @ 2.5 Ghz (C/C++)
41 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.
42 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.
43 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.
44 FCNB 90.21 % 86.28 % 89.67 % 90.77 % 11.50 % 9.23 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
45 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.
46 DVFCN 89.95 % 93.93 % 89.74 % 90.17 % 11.33 % 9.83 % 0.07 s GPU @ 2.5 Ghz (Python)
47 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.
48 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.
49 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.
50 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.
51 RFH 89.34 % 79.11 % 81.78 % 98.42 % 24.10 % 1.58 % .5 s 1 core @ 2.5 Ghz (C/C++)
52 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.
53 SegNet 88.59 % 83.54 % 88.35 % 88.84 % 12.88 % 11.16 % 0.01 s 8 cores @ 2.5 Ghz (C/C++)
54 PFH+HSV 88.39 % 83.32 % 90.20 % 86.65 % 10.35 % 13.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 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++)
56 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.
57 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.
58 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.
59 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.
60 LWD 86.14 % 88.31 % 86.99 % 85.31 % 14.03 % 14.69 % 0.07 s GPU @ 2.5 Ghz (Python)
61 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++)
62 RNF 83.87 % 78.26 % 84.02 % 83.72 % 17.51 % 16.28 % .5 s 1 core @ 2.5 Ghz (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 INM 83.48 % 82.35 % 93.60 % 75.34 % 5.66 % 24.66 % 5 s 1 core @ 2.5 Ghz (C/C++)
65 NSV 83.11 % 80.65 % 73.97 % 94.83 % 36.68 % 5.17 % 5 s GPU @ 3.5 Ghz (Matlab)
66 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.
67 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.
68 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.
Table as LaTeX | Only published Methods

UU_ROAD


Method Setting Code MaxF AP PRE REC FPR FNR Runtime Environment
1 UNV 95.71 % 90.32 % 95.13 % 96.30 % 1.61 % 3.70 % 1.2 s GPU @ 3.0 Ghz (Python + C/C++)
2 RPP 95.47 % 88.74 % 95.16 % 95.77 % 1.59 % 4.23 % 0.16 s GPU (C/C++)
3 TuSimple 95.17 % 92.73 % 95.97 % 94.39 % 1.29 % 5.61 % 0.2s GPU @ 2.5 Ghz (Python)
4 SAIT 95.06 % 87.59 % 93.89 % 96.25 % 2.04 % 3.75 % 0.04 s GPU @ 2.5 Ghz (C/C++)
5 SAIT 95.01 % 88.58 % 94.98 % 95.04 % 1.64 % 4.96 % 0.04 s GPU @ 2.5 Ghz (C/C++)
6 CoDNN 93.81 % 92.41 % 94.20 % 93.43 % 1.88 % 6.57 % 0.01 s 1 core @ 2.5 Ghz (Python + C/C++)
7 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.
8 FF 93.62 % 89.14 % 95.59 % 91.72 % 1.38 % 8.28 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
9 uickitti 93.58 % 92.14 % 93.34 % 93.83 % 2.18 % 6.17 % 0.1 s GPU @ 1.0 Ghz (Python)
10 RPN 93.21 % 89.18 % 92.81 % 93.60 % 2.36 % 6.40 % 0.18 s GPU @ 2.5 Ghz (Matlab + C/C++)
11 SSL 93.19 % 88.17 % 92.99 % 93.41 % 2.30 % 6.59 % 0.05 s GPU @ 2.5 Ghz (C/C++)
12 AXN 92.97 % 91.91 % 92.68 % 93.27 % 2.40 % 6.73 % 0.06 s GPU @ 2.5 Ghz (C/C++)
13 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++)
14 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++)
15 Up-Conv-Poly 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.
16 RDBN 92.51 % 89.75 % 92.95 % 92.08 % 2.28 % 7.92 % 0.25 s GPU @ 2.5 Ghz (Matlab + C/C++)
17 TDCac1 CNN 92.30 % 86.21 % 92.37 % 92.23 % 2.48 % 7.77 % .093 s 1 core @ 1.0 Ghz (C/C++)
18 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.
19 FCN-GCBs 92.10 % 83.69 % 89.61 % 94.73 % 3.58 % 5.27 % 0.08 s GPU @ 2.5 Ghz (C/C++)
20 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.
21 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.
22 JOJnet 90.72 % 86.26 % 91.32 % 90.13 % 2.79 % 9.87 % 0.1 s GPU @ 2.5 Ghz (C/C++)
23 s-FCN-loc 90.42 % 80.07 % 92.40 % 88.52 % 2.37 % 11.48 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
24 VPD
This method uses stereo information.
90.03 % 78.89 % 90.96 % 89.11 % 2.89 % 10.89 % 4 s 1 core @ 3.0 Ghz (Matlab + C/C++)
25 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.
26 VGGFCN 88.90 % 76.87 % 88.49 % 89.31 % 3.79 % 10.69 % 0.4 s GPU @ 1.0 Ghz (Python + C/C++)
27 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 and B. Dai: Hybrid Conditional Random Field based Camera-LIDAR Fusion for Road Detection. Information Sciences. in press.
28 SGL 88.40 % 78.31 % 90.25 % 86.63 % 3.05 % 13.37 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
29 Feature++
This method uses stereo information.
87.40 % 85.48 % 86.19 % 88.64 % 4.63 % 11.36 % 13 s 4 core @ 2.5 Ghz (Matlab + C/C++)
30 LidarHisto
This method makes use of Velodyne laser scans.
86.55 % 81.13 % 90.71 % 82.75 % 2.76 % 17.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
31 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.
32 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.
33 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.
34 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.
35 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.
36 BNV
This method uses stereo information.
85.46 % 74.07 % 85.06 % 85.86 % 4.91 % 14.14 % 3 s 2 cores @ 2.5 Ghz (C/C++)
37 DVFCN 85.37 % 89.13 % 85.44 % 85.30 % 4.74 % 14.70 % 0.07 s GPU @ 2.5 Ghz (Python)
38 FCNB 85.29 % 73.20 % 84.01 % 86.62 % 5.37 % 13.38 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
39 LWD 84.96 % 87.51 % 82.18 % 87.93 % 6.21 % 12.07 % 0.07 s GPU @ 2.5 Ghz (Python)
40 LWDS 84.50 % 83.24 % 87.46 % 81.72 % 3.82 % 18.28 % 0.07 s GPU @ 2.5 Ghz (Python)
41 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.
42 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.
43 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.
44 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.
45 Multimodal 83.01 % 87.47 % 80.20 % 86.02 % 6.92 % 13.98 % 0.3 s 4 cores @ 2.5 Ghz (Matlab)
46 MixedCRF
This method makes use of Velodyne laser scans.
82.79 % 69.11 % 79.01 % 86.96 % 7.53 % 13.04 % 6s 1 core @ 2.5 Ghz (Matlab + C/C++)
47 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.
48 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.
49 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++)
50 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.
51 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.
52 FCNS 80.20 % 84.90 % 77.92 % 82.62 % 7.63 % 17.38 % 6 s 1 core @ 2.5 Ghz (C/C++)
53 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.
54 PFH+HSV 79.61 % 66.78 % 82.60 % 76.83 % 5.27 % 23.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 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.
56 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++)
57 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.
58 SegNet 77.23 % 69.23 % 82.29 % 72.76 % 5.10 % 27.24 % 0.01 s 8 cores @ 2.5 Ghz (C/C++)
59 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.
60 RNF 75.36 % 63.87 % 78.61 % 72.37 % 6.42 % 27.63 % .5 s 1 core @ 2.5 Ghz (C/C++)
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 INM 72.90 % 64.53 % 87.36 % 62.55 % 2.95 % 37.45 % 5 s 1 core @ 2.5 Ghz (C/C++)
65 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.
66 RFH 71.69 % 55.20 % 62.01 % 84.96 % 16.96 % 15.04 % .5 s 1 core @ 2.5 Ghz (C/C++)
67 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.
68 NSV 69.53 % 65.34 % 58.13 % 86.51 % 20.30 % 13.49 % 5 s GPU @ 3.5 Ghz (Matlab)
69 ANN
This method uses stereo information.
54.07 % 36.61 % 39.28 % 86.69 % 43.67 % 13.31 % 3 s 1 core @ 3.0 Ghz (C/C++)
G. Vitor, D. Lima, A. Victorino and J. Ferreira: A 2D/3D Vision Based Approach Applied to Road Detection in Urban Environments. Intelligent Vehicles Symposium (IV), 2013 IEEE 2013.
Table as LaTeX | Only published Methods

URBAN_ROAD


Method Setting Code MaxF AP PRE REC FPR FNR Runtime Environment
1 UNV 96.74 % 92.39 % 96.88 % 96.60 % 1.71 % 3.40 % 1.2 s GPU @ 3.0 Ghz (Python + C/C++)
2 TuSimple 96.41 % 93.88 % 96.44 % 96.37 % 1.96 % 3.63 % 0.2s GPU @ 2.5 Ghz (Python)
3 RPP 96.36 % 90.36 % 95.85 % 96.87 % 2.31 % 3.13 % 0.16 s GPU (C/C++)
4 SAIT 96.27 % 90.34 % 95.83 % 96.72 % 2.32 % 3.28 % 0.04 s GPU @ 2.5 Ghz (C/C++)
5 SAIT 96.02 % 90.72 % 96.24 % 95.79 % 2.06 % 4.21 % 0.04 s GPU @ 2.5 Ghz (C/C++)
6 CoDNN 95.06 % 93.58 % 95.24 % 94.88 % 2.61 % 5.12 % 0.01 s 1 core @ 2.5 Ghz (Python + C/C++)
7 RPN 94.97 % 91.49 % 94.94 % 95.01 % 2.79 % 4.99 % 0.18 s GPU @ 2.5 Ghz (Matlab + C/C++)
8 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.
9 TDCac1 CNN 94.81 % 89.82 % 95.25 % 94.38 % 2.59 % 5.62 % .093 s 1 core @ 1.0 Ghz (C/C++)
10 AXN 94.69 % 93.36 % 94.70 % 94.69 % 2.92 % 5.31 % 0.06 s GPU @ 2.5 Ghz (C/C++)
11 SSL 94.69 % 90.51 % 94.89 % 94.49 % 2.81 % 5.51 % 0.05 s GPU @ 2.5 Ghz (C/C++)
12 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++)
13 uickitti 94.63 % 93.56 % 94.38 % 94.88 % 3.11 % 5.12 % 0.1 s GPU @ 1.0 Ghz (Python)
14 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++)
15 RDBN 94.15 % 91.74 % 94.88 % 93.44 % 2.78 % 6.56 % 0.25 s GPU @ 2.5 Ghz (Matlab + C/C++)
16 FCN-GCBs 94.08 % 87.66 % 92.87 % 95.32 % 4.03 % 4.68 % 0.08 s GPU @ 2.5 Ghz (C/C++)
17 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.
18 FF 94.02 % 90.34 % 95.82 % 92.28 % 2.22 % 7.72 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
19 Up-Conv-Poly 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.
20 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.
21 s-FCN-loc 93.26 % 88.83 % 94.16 % 92.39 % 3.16 % 7.61 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
22 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.
23 JOJnet 92.19 % 87.56 % 91.71 % 92.68 % 4.61 % 7.32 % 0.1 s GPU @ 2.5 Ghz (C/C++)
24 VGGFCN 91.95 % 86.42 % 91.51 % 92.40 % 4.72 % 7.61 % 0.4 s GPU @ 1.0 Ghz (Python + C/C++)
25 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.
26 Feature++
This method uses stereo information.
91.12 % 89.51 % 90.16 % 92.10 % 5.54 % 7.90 % 13 s 4 core @ 2.5 Ghz (Matlab + C/C++)
27 SGL 90.99 % 82.67 % 93.15 % 88.92 % 3.60 % 11.08 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
28 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 and B. Dai: Hybrid Conditional Random Field based Camera-LIDAR Fusion for Road Detection. Information Sciences. in press.
29 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.
30 LidarHisto
This method makes use of Velodyne laser scans.
90.67 % 84.79 % 93.06 % 88.41 % 3.63 % 11.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
31 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.
32 LWDS 89.83 % 87.04 % 91.61 % 88.12 % 4.45 % 11.88 % 0.07 s GPU @ 2.5 Ghz (Python)
33 BNV
This method uses stereo information.
89.75 % 84.15 % 89.02 % 90.49 % 6.15 % 9.51 % 3 s 2 cores @ 2.5 Ghz (C/C++)
34 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.
35 MixedCRF
This method makes use of Velodyne laser scans.
89.46 % 83.70 % 88.52 % 90.42 % 6.46 % 9.59 % 6s 1 core @ 2.5 Ghz (Matlab + C/C++)
36 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.
37 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.
38 FCNB 88.35 % 78.22 % 87.71 % 88.99 % 6.87 % 11.01 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
39 DVFCN 88.34 % 91.70 % 88.51 % 88.17 % 6.30 % 11.83 % 0.07 s GPU @ 2.5 Ghz (Python)
40 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.
41 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.
42 Multimodal 87.78 % 91.25 % 86.40 % 89.21 % 7.74 % 10.79 % 0.3 s 4 cores @ 2.5 Ghz (Matlab)
43 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.
44 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.
45 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.
46 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.
47 FCNS 85.93 % 89.80 % 84.59 % 87.32 % 8.76 % 12.68 % 6 s 1 core @ 2.5 Ghz (C/C++)
48 LWD 85.74 % 84.86 % 83.76 % 87.81 % 9.38 % 12.19 % 0.07 s GPU @ 2.5 Ghz (Python)
49 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.
50 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++)
51 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.
52 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.
53 PFH+HSV 84.67 % 77.38 % 86.68 % 82.76 % 7.00 % 17.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
54 SegNet 84.04 % 78.76 % 85.50 % 82.63 % 7.72 % 17.37 % 0.01 s 8 cores @ 2.5 Ghz (C/C++)
55 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.
56 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.
57 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.
58 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++)
59 RNF 81.79 % 70.72 % 83.92 % 79.77 % 8.42 % 20.23 % .5 s 1 core @ 2.5 Ghz (C/C++)
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 RFH 80.94 % 69.22 % 72.59 % 91.46 % 19.03 % 8.54 % .5 s 1 core @ 2.5 Ghz (C/C++)
62 INM 80.74 % 76.15 % 91.39 % 72.32 % 3.75 % 27.68 % 5 s 1 core @ 2.5 Ghz (C/C++)
63 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.
64 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.
65 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.
66 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.
67 NSV 76.23 % 72.67 % 65.46 % 91.24 % 26.51 % 8.76 % 5 s GPU @ 3.5 Ghz (Matlab)
68 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.
Table as LaTeX | Only published Methods

Lane Estimation Evaluation

UM_LANE


Method Setting Code MaxF AP PRE REC FPR FNR Runtime Environment
1 SAIT 93.25 % 87.94 % 95.24 % 91.34 % 0.80 % 8.66 % 0.04 s GPU @ 2.5 Ghz (C/C++)
2 SAIT 92.29 % 88.39 % 93.31 % 91.28 % 1.15 % 8.72 % 0.04 s GPU @ 2.5 Ghz (C/C++)
3 SSL 92.29 % 88.39 % 93.31 % 91.28 % 1.15 % 8.72 % 0.05 s GPU @ 2.5 Ghz (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 S-Lane 91.76 % 82.37 % 95.96 % 87.92 % 0.65 % 12.08 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
6 RDBN 91.55 % 81.62 % 94.27 % 88.98 % 0.95 % 11.02 % 0.25 s GPU @ 2.5 Ghz (Matlab + C/C++)
7 FCN-GCBs 91.24 % 85.14 % 92.15 % 90.35 % 1.35 % 9.65 % 0.08 s GPU @ 2.5 Ghz (C/C++)
8 RPN 90.54 % 82.03 % 94.92 % 86.56 % 0.82 % 13.44 % 0.18 s GPU @ 2.5 Ghz (Matlab + C/C++)
9 JOJnet 90.49 % 83.84 % 89.97 % 91.01 % 1.79 % 8.99 % 0.1 s GPU @ 2.5 Ghz (C/C++)
10 VPD
This method uses stereo information.
90.01 % 81.60 % 88.26 % 91.82 % 2.15 % 8.18 % 4 s 1 core @ 3.0 Ghz (Matlab + C/C++)
11 Up-Conv-Poly 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.
12 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.
13 FCNS 76.31 % 81.57 % 77.88 % 74.81 % 3.74 % 25.19 % 6 s 1 core @ 2.5 Ghz (C/C++)
14 FCNB 75.85 % 79.27 % 71.49 % 80.77 % 5.67 % 19.23 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
15 PFH+HSV 75.85 % 79.27 % 71.49 % 80.77 % 5.67 % 19.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
16 RPP 75.85 % 79.27 % 71.49 % 80.77 % 5.67 % 19.23 % 0.16 s GPU (C/C++)
17 FF 75.85 % 79.27 % 71.49 % 80.77 % 5.67 % 19.23 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
18 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.
19 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 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++)
3 SAIT 99.23 % 99.32 % 99.13 % 98.71 % 97.41 % 95.93 % 95.65 % 87.36 % 80.26 % 0.04 s GPU @ 2.5 Ghz (C/C++)
4 RPN 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++)
5 SAIT 99.21 % 99.24 % 99.08 % 98.83 % 97.34 % 95.85 % 95.65 % 81.61 % 78.95 % 0.04 s GPU @ 2.5 Ghz (C/C++)
6 SSL 99.21 % 99.24 % 99.08 % 98.83 % 97.34 % 95.85 % 95.65 % 81.61 % 78.95 % 0.05 s GPU @ 2.5 Ghz (C/C++)
7 VPD
This method uses stereo information.
99.12 % 98.87 % 97.71 % 99.07 % 97.93 % 95.58 % 96.70 % 90.70 % 78.67 % 4 s 1 core @ 3.0 Ghz (Matlab + C/C++)
8 S-Lane 99.23 % 99.43 % 99.38 % 98.30 % 96.82 % 95.22 % 93.33 % 87.06 % 81.33 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
9 RDBN 99.19 % 99.32 % 99.21 % 98.05 % 96.48 % 95.07 % 94.51 % 84.88 % 77.63 % 0.25 s GPU @ 2.5 Ghz (Matlab + C/C++)
10 JOJnet 99.18 % 99.26 % 98.94 % 98.10 % 96.38 % 95.03 % 93.48 % 86.21 % 84.21 % 0.1 s GPU @ 2.5 Ghz (C/C++)
11 Up-Conv-Poly 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.
12 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.
13 FCNS 98.72 % 98.29 % 97.58 % 94.67 % 90.59 % 87.28 % 82.35 % 66.67 % 64.29 % 6 s 1 core @ 2.5 Ghz (C/C++)
14 FCNB 95.83 % 92.92 % 89.16 % 94.41 % 89.44 % 83.62 % 84.04 % 66.67 % 50.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
15 PFH+HSV 95.83 % 92.92 % 89.16 % 94.41 % 89.44 % 83.62 % 84.04 % 66.67 % 50.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
16 RPP 95.83 % 92.92 % 89.16 % 94.41 % 89.44 % 83.62 % 84.04 % 66.67 % 50.00 % 0.16 s GPU (C/C++)
17 FF 95.83 % 92.92 % 89.16 % 94.41 % 89.44 % 83.62 % 84.04 % 66.67 % 50.00 % 0.33 s 1 core @ 2.5 Ghz (C/C++)
18 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.
19 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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