The KITTI Vision Benchmark Suite

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

Table as LaTeX | Only published Methods

UMM_ROAD

Table as LaTeX | Only published Methods

UU_ROAD

Table as LaTeX | Only published Methods

URBAN_ROAD

Table as LaTeX | Only published Methods

Lane Estimation Evaluation

UM_LANE

	Method	Setting	Code	MaxF	AP	PRE	REC	FPR	FNR	Runtime	Environment
1	CyberMELD+PLARD		code	94.44 %	88.59 %	95.95 %	92.97 %	0.69 %	7.03 %	0.18 s	8 cores @ 1.5 Ghz (Python + C/C++)
X. Wang, Y. Qian, C. Wang and M. Yang: Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios. IEEE Access 2020.
2	CyberMELD		code	93.56 %	88.58 %	95.94 %	91.30 %	0.68 %	8.70 %	0.05 s	8 core @ 1.5 Ghz (C/C++)
X. Wang, Y. Qian, C. Wang and M. Yang: Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios. IEEE Access 2020.
3	RoadNet3			91.47 %	91.01 %	91.78 %	91.17 %	1.44 %	8.83 %	300 ms	GPU @ GTX950M (Python +Tensorflow)
Y. Lyu, L. Bai and X. Huang: Road Segmentation using CNN and Distributed LSTM. 2019 IEEE International Symposium on Circuits and Systems (ISCAS) 2019.
4	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.
5	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.
6	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.
7	SPlane + BL			69.63 %	73.78 %	80.01 %	61.63 %	2.71 %	38.37 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
8	SCRFFPFHGSP			57.22 %	39.34 %	41.78 %	90.79 %	22.28 %	9.21 %	5 s	8 cores @ 2.5 Ghz (C/C++, Matlab)
I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.

Table as LaTeX | Only published Methods

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	CyberMELD		code	99.17 %	99.23 %	99.11 %	98.64 %	98.00 %	97.55 %	94.57 %	89.66 %	90.79 %	0.05 s	8 core @ 1.5 Ghz (C/C++)
X. Wang, Y. Qian, C. Wang and M. Yang: Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios. IEEE Access 2020.
2	CyberMELD+PLARD		code	99.18 %	99.36 %	99.29 %	98.70 %	98.20 %	97.17 %	96.74 %	90.80 %	90.79 %	0.18 s	8 cores @ 1.5 Ghz (Python + C/C++)
X. Wang, Y. Qian, C. Wang and M. Yang: Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios. IEEE Access 2020.
3	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.
4	RoadNet3			99.18 %	99.21 %	99.07 %	98.39 %	97.23 %	95.57 %	94.57 %	83.72 %	80.26 %	300 ms	GPU @ GTX950M (Python +Tensorflow)
Y. Lyu, L. Bai and X. Huang: Road Segmentation using CNN and Distributed LSTM. 2019 IEEE International Symposium on Circuits and Systems (ISCAS) 2019.
5	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.
6	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.
7	SPlane + BL			95.53 %	92.88 %	91.21 %	91.89 %	87.12 %	74.28 %	79.79 %	47.13 %	0.00 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
8	SCRFFPFHGSP			94.88 %	87.95 %	82.98 %	87.91 %	78.90 %	71.95 %	60.64 %	43.68 %	38.16 %	5 s	8 cores @ 2.5 Ghz (C/C++, Matlab)
I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.

Table as LaTeX | Only published Methods

Related Datasets

Multi-Lane-Detection-Dataset: Dataset for multiple lane detection.
Road Scene Layout from a Single Image: Dataset for road area estimation.
MIT Street Scenes: Dataset for semantic road scene understanding.
Cambridge-driving Labeled Video Database (CamVid): Dataset for semantic road scene understanding.
Daimler Scene Labeling Dataset: Dataset for semantic road scene understanding including stereo images.
ROMA (ROad MArkings): Dataset for performance evaluation of road marking extraction algorithms.

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}
}

	Method	Setting	Code	MaxF	AP	PRE	REC	FPR	FNR	Runtime	Environment
1	SNE-RoadSegV2			97.25 %	93.52 %	97.48 %	97.03 %	1.14 %	2.97 %	0.03 s	GPU @ 2.5 Ghz (Python)
Y. Feng, Y. Ma, Q. Chen, I. Pitas and R. Fan: SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection. 2024.
2	RoadFormer+			97.17 %	93.41 %	97.09 %	97.24 %	1.33 %	2.76 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

3	PLARD		code	97.05 %	93.53 %	97.18 %	96.92 %	1.28 %	3.08 %	0.16 s	GPU @ 2.5 Ghz (Python)
Z. Chen, J. Zhang and D. Tao: Progressive LiDAR adaptation for road detection. IEEE/CAA Journal of Automatica Sinica 2019.
4	RoadFormer			97.02 %	93.34 %	96.84 %	97.20 %	1.45 %	2.80 %	0.07 s	GPU @ 2.5 Ghz (Python)
J. Li, Y. Zhang, P. Yun, G. Zhou, Q. Chen and R. Fan: RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing. IEEE Transactions on Intelligent Vehicles 2024.
5	SNE-RoadSeg+			96.95 %	93.60 %	96.99 %	96.90 %	1.37 %	3.10 %	0.08 s	GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan, P. Cai and M. Liu: SNE-RoadSeg+: Rethinking depth-normal translation and deep supervision for freespace detection. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021.
6	PLB-RD			96.87 %	93.71 %	97.35 %	96.40 %	1.20 %	3.60 %	0.46 s	GPU @ 2.5 Ghz (Python)
L. Sun, H. Zhang and W. Yin: Pseudo-LiDAR-Based Road Detection. IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT) 2022.
7	CLCFNet-SD			96.57 %	90.71 %	96.65 %	96.49 %	1.53 %	3.51 %	0.05 s	GPU @ 2.5 Ghz (Python)

8	USNet		code	96.46 %	92.78 %	96.32 %	96.60 %	1.68 %	3.40 %	0.02 s	GPU @ 1.5 Ghz (Python)
Y. Chang, F. Xue, F. Sheng, W. Liang and A. Ming: Fast Road Segmentation via Uncertainty-aware Symmetric Network. IEEE International Conference on Robotics and Automation (ICRA) 2022.
9	DFM-RTFNet			96.46 %	93.66 %	96.58 %	96.33 %	1.55 %	3.67 %	0.08 s	GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan, Y. Sun and M. Liu: Dynamic fusion module evolves drivable area and road anomaly detection: A benchmark and algorithms. IEEE Transactions on Cybernetics 2021.
10	SNE-RoadSeg		code	96.42 %	93.67 %	96.59 %	96.26 %	1.55 %	3.74 %	0.18 s	GPU @ 2.5 Ghz (Python)
R. Fan, H. Wang, P. Cai and M. Liu: SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection. ECCV 2020.
11	Epurate-Net			96.39 %	92.28 %	95.75 %	97.05 %	1.96 %	2.95 %	0.02 s	GPU @ 2.5 Ghz (Python)

12	LRDNet+		code	96.10 %	92.00 %	96.89 %	95.32 %	1.39 %	4.68 %	0.01 s	GPU @ 2.5 Ghz (Python)
A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.
13	LRDNet(S)		code	96.01 %	92.47 %	96.60 %	95.43 %	1.53 %	4.57 %	.009 s	GPU @ 2.5 Ghz (Python)
A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.
14	LRDNet (L)		code	96.01 %	91.83 %	96.84 %	95.19 %	1.41 %	4.81 %	0.1 s	GPU @ 2.5 Ghz (Python)
A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.
15	RBANet			95.78 %	89.14 %	94.92 %	96.66 %	2.36 %	3.34 %	0.16 s	GPU @ 1.5 Ghz (Python + C/C++)
J. Sun, S. Kim, S. Lee, Y. Kim and S. Ko: Reverse and Boundary Attention Network for Road Segmentation. Proceedings of the IEEE International Conference on Computer Vision Workshops 2019.
16	NIM-RTFNet			95.71 %	93.56 %	95.84 %	95.59 %	1.89 %	4.41 %	0.05 s	GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan, Y. Sun and M. Liu: Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
17	CLCFNet			95.65 %	89.49 %	95.31 %	96.00 %	2.15 %	4.00 %	0.02 s	GPU @ 1.5 Ghz (Python)
S. Gu, J. Yang and H. Kong: A Cascaded LiDAR-Camera Fusion Network for Road Detection. ICRA 2021.
18	LidCamNet			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. Robotics and Autonomous Systems 2018.
19	CLCFNet-SD (LiDAR)			95.38 %	89.07 %	94.84 %	95.91 %	2.38 %	4.09 %	0.04 s	GPU @ 2.5 Ghz (Python)

20	CLCFNet (LiDAR)			95.16 %	89.18 %	94.97 %	95.36 %	2.30 %	4.64 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
S. Gu, J. Yang and H. Kong: A Cascaded LiDAR-Camera Fusion Network for Road Detection. ICRA 2021.
21	TVFNet			94.96 %	89.17 %	94.95 %	94.97 %	2.30 %	5.03 %	0.04 s	GPU @ 1.5 Ghz (Python)
S. Gu, Y. Zhang, J. Yang, J. Alvarez and H. Kong: Two-View Fusion based Convolutional Neural Network for Urban Road Detection. IROS 2019.
22	LC-CRF			94.91 %	86.41 %	91.92 %	98.11 %	3.93 %	1.89 %	0.18 s	GPU @ 1.5 Ghz (Python + C/C++)
S. Gu, Y. Zhang, J. Tang, J. Yang and H. Kong: Road Detection through CRF based LiDAR-Camera Fusion. ICRA 2019.
23	RBNet			94.77 %	91.42 %	95.16 %	94.37 %	2.19 %	5.63 %	0.18 s	GPU @ 2.5 Ghz (Matlab + C/C++)
Z. Chen and Z. Chen: RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection. International Conference on Neural Information Processing 2017.
24	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.
25	LFD-RoadSeg		code	94.58 %	93.42 %	95.20 %	93.98 %	2.16 %	6.02 %	.004 s	GPU @ 1.5 Ghz (Python)
H. Zhou, F. Xue, Y. Li, S. Gong, Y. Li and Y. Zhou: Exploiting Low-Level Representations for Ultra-Fast Road Segmentation. IEEE Transactions on Intelligent Transportation Systems 2024.
26	RGB36-Cotrain			94.55 %	93.12 %	94.81 %	94.29 %	2.35 %	5.71 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Caltagirone, S. Lennart, M. Wahde and M. Sanfridson: Lidar-Camera Co-Training for Semi- Supervised Road Detection. arXiv preprint arXiv:1911.12597 2019.
27	HA-DeepLabv3+			94.38 %	92.72 %	94.70 %	94.06 %	2.40 %	5.94 %	0.06 s	1 core @ 2.5 Ghz (Python)
R. Fan, H. Wang, P. Cai, J. Wu, M. Bocus, L. Qiao and M. Liu: Learning collision-free space detection from stereo images: Homography matrix brings better data augmentation. IEEE/ASME Transactions on Mechatronics 2021.
28	TEDNet			94.24 %	92.43 %	93.45 %	95.04 %	3.04 %	4.96 %	0.09 s	GPU @ 2.5 Ghz (Python)
M. Bayón-Gutiérrez, J. Benítez- Andrades, S. Rubio-Martín, J. Aveleira-Mata, H. Alaiz-Moretón and M. García-Ordás: Roadway Detection Using Convolutional Neural Network Through Camera and LiDAR Data. Hybrid Artificial Intelligent Systems 2022.
29	BJN			94.17 %	89.16 %	94.95 %	93.41 %	2.26 %	6.59 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
B. Yu, D. Lee, J. Lee and S. Kee: Free Space Detection Using Camera-LiDAR Fusion in a Bird’s Eye View Plane. Sensors 2021.
30	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.
31	CLRD			94.06 %	92.13 %	94.32 %	93.80 %	2.57 %	6.21 %	0.05 s	GPU @ 2.5 Ghz (Python)
M. Bayón-Gutiérrez, J. Benítez- Andrades, S. Rubio-Martín, J. Aveleira-Mata, H. Alaiz-Moretón and M. García-Ordás: Roadway Detection Using Convolutional Neural Network Through Camera and LiDAR Data. Hybrid Artificial Intelligent Systems 2022.
32	Hadamard-FCN			94.06 %	90.89 %	94.62 %	93.50 %	2.42 %	6.50 %	0.02 s	GPU @ 1.5 Ghz (Python)
M. Oeljeklaus: An Integrated Approach for Traffic Scene Understanding from Monocular Cameras. 2021.
33	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.
34	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.
35	ChipNet			93.73 %	87.62 %	93.25 %	94.21 %	3.11 %	5.79 %	12 ms	GPU @ 1.5 Ghz (Keras)
Y. Lyu, L. Bai and X. Huang: ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA. IEEE Transactions on Circuits and Systems I: Regular Papers 2019.
36	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.
37	RoadNet3			93.54 %	92.64 %	93.65 %	93.44 %	2.89 %	6.56 %	300 ms	GPU @ GTX950M (Python +Tensorflow)
Y. Lyu, L. Bai and X. Huang: Road Segmentation using CNN and Distributed LSTM. 2019 IEEE International Symposium on Circuits and Systems (ISCAS) 2019.
38	HID-LS			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 2019.
39	RGB36-Super			93.04 %	91.85 %	93.62 %	92.46 %	2.87 %	7.54 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Caltagirone, S. Lennart, M. Wahde and M. Sanfridson: Lidar-Camera Co-Training for Semi- Supervised Road Detection. arXiv preprint arXiv:1911.12597 2019.
40	LoDNN			92.75 %	89.98 %	90.09 %	95.58 %	4.79 %	4.42 %	18 ms	GPU @ 2.5 Ghz (Torch)
L. Caltagirone, S. Scheidegger, L. Svensson and M. Wahde: Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks. IEEE Intelligent Vehicles Symposium 2017.
41	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.
42	OFA Net		code	92.08 %	82.73 %	87.87 %	96.72 %	6.08 %	3.28 %	0.04 s	GPU @ 1.5 Ghz (Python)
S. Zhang, Z. Zhang, L. Sun and W. Qin: One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation. Applied Sciences 2019.
43	RoadNet-RT			91.99 %	92.54 %	92.75 %	91.24 %	3.25 %	8.76 %	8m s	GPU @ 2.5 Ghz (Python)
L. Bai, Y. Lyu and X. Huang: RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation. arXiv preprint arXiv:2006.07644 2020.
44	MixedCRF			91.57 %	84.68 %	90.02 %	93.19 %	4.71 %	6.81 %	6s	1 core @ 2.5 Ghz (Matlab + C/C++)
X. Han, H. Wang, J. Lu and C. Zhao: Road detection based on the fusion of Lidar and image data. 2017.
45	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.
46	ALO-AVG-MM		code	91.15 %	83.82 %	89.07 %	93.33 %	5.22 %	6.67 %	0.0296 sec	GeForce GTX 1080 GPU (Python)
F. Reis, R. Almeida, E. Kijak, S. Malinowski, S. Guimaraes and Z. Jr.: Combining convolutional side-outputs for road image segmentation. 2019 International Joint Conference on Neural Networks (IJCNN) - \textbfAccepted 2019.
47	HybridCRF			90.99 %	85.26 %	90.65 %	91.33 %	4.29 %	8.67 %	1.5 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, R. Wang, B. Dai, Y. Fang, D. Liu and T. Wu: Hybrid conditional random field based camera-LIDAR fusion for road detection. Information Sciences 2018.
48	NNP			90.50 %	87.95 %	91.43 %	89.59 %	3.83 %	10.41 %	5 s	4 cores @ 2.5 Ghz (Matlab)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
49	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.
50	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.
51	LidarHisto		code	89.87 %	83.03 %	91.28 %	88.49 %	3.85 %	11.51 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Chen, J. Yang and H. Kong: Lidar-histogram for fast road and obstacle detection. 2017 IEEE International Conference on Robotics and Automation (ICRA) 2017.
52	FusedCRF			89.55 %	80.00 %	84.87 %	94.78 %	7.70 %	5.22 %	2 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, B. Dai, D. Liu, T. Hu and T. Wu: CRF based Road Detection with Multi-Sensor Fusion. Intelligent Vehicles Symposium (IV) 2015.
53	BMCF			89.42 %	83.13 %	88.31 %	90.55 %	5.46 %	9.45 %	2.5 s	1 core @ 2.5 Ghz (C/C++)
L. Wang, T. Wu, Z. Xiao, L. Xiao, D. Zhao and J. Han: Multi-cue road boundary detection using stereo vision. 2016 IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2016.
54	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.
55	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.
56	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.
57	ProbBoost			87.48 %	80.13 %	85.02 %	90.09 %	7.23 %	9.91 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
G. Vitor, A. Victorino and J. Ferreira: A probabilistic distribution approach for the classification of urban roads in complex environments. Workshop on Modelling, Estimation, Perception and Control of All Terrain Mobile Robots on IEEE International Conference on Robotics and Automation (ICRA) 2014.
58	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.
59	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.
60	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.
61	GRES3D+VELO			85.43 %	83.04 %	82.69 %	88.37 %	8.43 %	11.63 %	60 ms	4 cores @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
62	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.
63	SPlane + BL			85.23 %	88.66 %	83.43 %	87.12 %	7.89 %	12.88 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
64	geo+gpr+crf			85.13 %	72.24 %	81.33 %	89.29 %	9.34 %	10.71 %	30 s	1 core @ 2.0 Ghz (C/C++)
Z. Xiao, B. Dai, H. Li, T. Wu, X. Xu, Y. Zeng and T. Chen: Gaussian process regression-based robust free space detection for autonomous vehicle by 3-D point cloud and 2-D appearance information fusion. International Journal of Advanced Robotic Systems 2017.
65	RES3D-Velo			83.81 %	73.95 %	78.56 %	89.80 %	11.16 %	10.20 %	0.36 s	1 core @ 2.5 Ghz (C/C++)
P. Shinzato, D. Wolf and C. Stiller: Road Terrain Detection: Avoiding Common Obstacle Detection Assumptions Using Sensor Fusion. Intelligent Vehicles Symposium (IV) 2014.
66	SCRFFPFHGSP			83.73 %	72.89 %	82.13 %	85.39 %	8.47 %	14.61 %	5 s	8 cores @ 2.5 Ghz (C/C++, Matlab)
I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.
67	GRES3D+SELAS			83.69 %	84.61 %	78.31 %	89.88 %	11.35 %	10.12 %	110 ms	4 core @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
68	HistonBoost			83.68 %	72.79 %	82.01 %	85.42 %	8.54 %	14.58 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
G. Vitor, A. Victorino and J. Ferreira: Comprehensive Performance Analysis of Road Detection Algorithms Using the Common Urban Kitti-Road Benchmark. Workshop on Benchmarking Road Terrain and Lane Detection Algorithms for In-Vehicle Application on IEEE Intelligent Vehicles Symposium (IV) 2014.
69	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.
70	RES3D-Stereo			78.98 %	80.06 %	75.94 %	82.27 %	11.88 %	17.73 %	0.7 s	1 core @ 2.5 Ghz (C/C++)
P. Shinzato, D. Gomes and D. Wolf: Road estimation with sparse 3D points from stereo data. Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on 2014.
71	BM			78.90 %	66.06 %	69.53 %	91.19 %	18.21 %	8.81 %	2 s	2 cores @ 2.5 Ghz (Matlab)
B. WANG, V. Fremont and S. Rodriguez Florez: Color-based Road Detection and its Evaluation on the KITTI Road Benchmark. Workshop on Benchmarking Road Terrain and Lane Detection Algorithms for In-Vehicle Application, IEEE Intelligent Vehicles Symposium 2014.
72	SPlane			78.19 %	76.41 %	72.03 %	85.50 %	15.13 %	14.50 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
73	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.
74	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.
75	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.
76	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.
77	ANN			62.83 %	46.77 %	50.21 %	83.91 %	37.91 %	16.09 %	3 s	1 core @ 3.0 Ghz (C/C++)
G. Vitor, D. Lima, A. Victorino and J. Ferreira: A 2D/3D Vision Based Approach Applied to Road Detection in Urban Environments. Intelligent Vehicles Symposium (IV), 2013 IEEE 2013.

	Method	Setting	Code	MaxF	AP	PRE	REC	FPR	FNR	Runtime	Environment
1	RoadFormer+			98.15 %	95.46 %	98.07 %	98.23 %	2.12 %	1.77 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

2	RoadFormer			98.15 %	95.60 %	98.07 %	98.23 %	2.13 %	1.77 %	0.07 s	GPU @ 2.5 Ghz (Python)
J. Li, Y. Zhang, P. Yun, G. Zhou, Q. Chen and R. Fan: RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing. IEEE Transactions on Intelligent Vehicles 2024.
3	SNE-RoadSeg+			98.13 %	95.52 %	98.01 %	98.25 %	2.19 %	1.75 %	0.08 s	GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan, P. Cai and M. Liu: SNE-RoadSeg+: Rethinking depth-normal translation and deep supervision for freespace detection. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021.
4	SNE-RoadSegV2			98.10 %	95.63 %	97.98 %	98.22 %	2.23 %	1.78 %	0.03 s	GPU @ 2.5 Ghz (Python)
Y. Feng, Y. Ma, Q. Chen, I. Pitas and R. Fan: SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection. 2024.
5	PLB-RD			98.05 %	95.63 %	97.89 %	98.21 %	2.33 %	1.79 %	0.46 s	GPU @ 2.5 Ghz (Python)
L. Sun, H. Zhang and W. Yin: Pseudo-LiDAR-Based Road Detection. IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT) 2022.
6	LRDNet+		code	97.98 %	94.28 %	97.67 %	98.29 %	2.58 %	1.71 %	0.01 s	GPU @ 2.5 Ghz (Python)
A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.
7	CLCFNet-SD			97.94 %	93.79 %	97.93 %	97.95 %	2.27 %	2.05 %	0.05 s	GPU @ 2.5 Ghz (Python)

8	Epurate-Net			97.91 %	95.16 %	97.75 %	98.08 %	2.48 %	1.92 %	0.02 s	GPU @ 2.5 Ghz (Python)

9	LRDNet (L)		code	97.91 %	93.96 %	97.45 %	98.37 %	2.83 %	1.63 %	0.1 s	GPU @ 2.5 Ghz (Python)
A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.
10	LRDNet(S)		code	97.82 %	94.29 %	97.39 %	98.25 %	2.90 %	1.75 %	.009 s	GPU @ 2.5 Ghz (Python)
A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.
11	PLARD		code	97.77 %	95.64 %	97.75 %	97.79 %	2.48 %	2.21 %	0.16 s	GPU @ 2.5 Ghz (Python)
Z. Chen, J. Zhang and D. Tao: Progressive LiDAR adaptation for road detection. IEEE/CAA Journal of Automatica Sinica 2019.
12	USNet		code	97.68 %	95.13 %	97.28 %	98.09 %	3.02 %	1.91 %	0.02 s	GPU @ 1.5 Ghz (Python)
Y. Chang, F. Xue, F. Sheng, W. Liang and A. Ming: Fast Road Segmentation via Uncertainty-aware Symmetric Network. IEEE International Conference on Robotics and Automation (ICRA) 2022.
13	SNE-RoadSeg		code	97.47 %	95.63 %	97.32 %	97.61 %	2.96 %	2.39 %	0.18 s	GPU @ 2.5 Ghz (Python)
R. Fan, H. Wang, P. Cai and M. Liu: SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection. ECCV 2020.
14	DFM-RTFNet			97.45 %	95.63 %	97.33 %	97.58 %	2.94 %	2.42 %	0.08 s	GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan, Y. Sun and M. Liu: Dynamic fusion module evolves drivable area and road anomaly detection: A benchmark and algorithms. IEEE Transactions on Cybernetics 2021.
15	RBANet			97.38 %	92.67 %	96.70 %	98.08 %	3.68 %	1.92 %	0.16 s	GPU @ 1.5 Ghz (Python + C/C++)
J. Sun, S. Kim, S. Lee, Y. Kim and S. Ko: Reverse and Boundary Attention Network for Road Segmentation. Proceedings of the IEEE International Conference on Computer Vision Workshops 2019.
16	CLCFNet-SD (LiDAR)			97.33 %	93.87 %	98.02 %	96.65 %	2.15 %	3.35 %	0.04 s	GPU @ 2.5 Ghz (Python)

17	CLCFNet			97.24 %	93.84 %	97.99 %	96.51 %	2.18 %	3.49 %	0.02 s	GPU @ 1.5 Ghz (Python)
S. Gu, J. Yang and H. Kong: A Cascaded LiDAR-Camera Fusion Network for Road Detection. ICRA 2021.
18	LC-CRF			97.08 %	92.06 %	96.03 %	98.16 %	4.46 %	1.84 %	0.18 s	GPU @ 1.5 Ghz (Python + C/C++)
S. Gu, Y. Zhang, J. Tang, J. Yang and H. Kong: Road Detection through CRF based LiDAR-Camera Fusion. ICRA 2019.
19	LidCamNet			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. Robotics and Autonomous Systems 2018.
20	CLCFNet (LiDAR)			96.88 %	93.71 %	97.85 %	95.94 %	2.32 %	4.06 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
S. Gu, J. Yang and H. Kong: A Cascaded LiDAR-Camera Fusion Network for Road Detection. ICRA 2021.
21	NIM-RTFNet			96.79 %	95.61 %	97.03 %	96.54 %	3.25 %	3.46 %	0.05 s	GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan, Y. Sun and M. Liu: Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
22	RGB36-Cotrain			96.75 %	95.39 %	96.84 %	96.66 %	3.46 %	3.34 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Caltagirone, S. Lennart, M. Wahde and M. Sanfridson: Lidar-Camera Co-Training for Semi- Supervised Road Detection. arXiv preprint arXiv:1911.12597 2019.
23	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.
24	LFD-RoadSeg		code	96.59 %	95.40 %	96.29 %	96.90 %	4.11 %	3.10 %	.004 s	GPU @ 1.5 Ghz (Python)
H. Zhou, F. Xue, Y. Li, S. Gong, Y. Li and Y. Zhou: Exploiting Low-Level Representations for Ultra-Fast Road Segmentation. IEEE Transactions on Intelligent Transportation Systems 2024.
25	TVFNet			96.47 %	93.16 %	97.24 %	95.71 %	2.98 %	4.29 %	0.04 s	GPU @ 1.5 Ghz (Python)
S. Gu, Y. Zhang, J. Yang, J. Alvarez and H. Kong: Two-View Fusion based Convolutional Neural Network for Urban Road Detection. IROS 2019.
26	BJN			96.29 %	93.98 %	98.14 %	94.52 %	1.97 %	5.48 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
B. Yu, D. Lee, J. Lee and S. Kee: Free Space Detection Using Camera-LiDAR Fusion in a Bird’s Eye View Plane. Sensors 2021.
27	Hadamard-FCN			96.26 %	93.32 %	95.63 %	96.90 %	4.86 %	3.10 %	0.02 s	GPU @ 1.5 Ghz (Python)
M. Oeljeklaus: An Integrated Approach for Traffic Scene Understanding from Monocular Cameras. 2021.
28	StixelNet II			96.22 %	91.24 %	95.13 %	97.33 %	5.48 %	2.67 %	1.2 s	1 core @ 3.0 Ghz (Matlab + C/C++)
N. Garnett, S. Silberstein, S. Oron, E. Fetaya, U. Verner, A. Ayash, V. Goldner, R. Cohen, K. Horn and D. Levi: Real-time category-based and general obstacle detection for autonomous driving. 5th Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving (CVRSUAD'17, IEEE-ICCV 2017 Workshop) 2017.
29	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.
30	HA-DeepLabv3+			96.10 %	95.03 %	95.48 %	96.73 %	5.03 %	3.27 %	0.06 s	1 core @ 2.5 Ghz (Python)
R. Fan, H. Wang, P. Cai, J. Wu, M. Bocus, L. Qiao and M. Liu: Learning collision-free space detection from stereo images: Homography matrix brings better data augmentation. IEEE/ASME Transactions on Mechatronics 2021.
31	RBNet			96.06 %	93.49 %	95.80 %	96.31 %	4.64 %	3.69 %	0.18 s	GPU @ 2.5 Ghz (Matlab + C/C++)
Z. Chen and Z. Chen: RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection. International Conference on Neural Information Processing 2017.
32	LoDNN			96.05 %	95.03 %	95.79 %	96.31 %	4.66 %	3.69 %	18 ms	GPU @ 2.5 Ghz (Torch)
L. Caltagirone, S. Scheidegger, L. Svensson and M. Wahde: Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks. IEEE Intelligent Vehicles Symposium 2017.
33	TEDNet			95.94 %	95.31 %	96.21 %	95.67 %	4.14 %	4.33 %	0.09 s	GPU @ 2.5 Ghz (Python)
M. Bayón-Gutiérrez, J. Benítez- Andrades, S. Rubio-Martín, J. Aveleira-Mata, H. Alaiz-Moretón and M. García-Ordás: Roadway Detection Using Convolutional Neural Network Through Camera and LiDAR Data. Hybrid Artificial Intelligent Systems 2022.
34	RoadNet3			95.88 %	95.46 %	96.37 %	95.40 %	3.95 %	4.60 %	300 ms	GPU @ GTX950M (Python +Tensorflow)
Y. Lyu, L. Bai and X. Huang: Road Segmentation using CNN and Distributed LSTM. 2019 IEEE International Symposium on Circuits and Systems (ISCAS) 2019.
35	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.
36	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.
37	OFA Net		code	95.43 %	89.10 %	92.78 %	98.24 %	8.41 %	1.76 %	0.04 s	GPU @ 1.5 Ghz (Python)
S. Zhang, Z. Zhang, L. Sun and W. Qin: One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation. Applied Sciences 2019.
38	CLRD			95.41 %	94.83 %	95.23 %	95.59 %	5.26 %	4.41 %	0.05 s	GPU @ 2.5 Ghz (Python)
M. Bayón-Gutiérrez, J. Benítez- Andrades, S. Rubio-Martín, J. Aveleira-Mata, H. Alaiz-Moretón and M. García-Ordás: Roadway Detection Using Convolutional Neural Network Through Camera and LiDAR Data. Hybrid Artificial Intelligent Systems 2022.
39	HID-LS			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 2019.
40	ChipNet			94.87 %	91.31 %	95.21 %	94.53 %	5.23 %	5.47 %	12 ms	GPU @ 1.5 Ghz (Keras)
Y. Lyu, L. Bai and X. Huang: ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA. IEEE Transactions on Circuits and Systems I: Regular Papers 2019.
41	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.
42	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.
43	ALO-AVG-MM		code	94.05 %	90.96 %	94.82 %	93.29 %	5.60 %	6.71 %	0.0296 sec	GeForce GTX 1080 GPU (Python)
F. Reis, R. Almeida, E. Kijak, S. Malinowski, S. Guimaraes and Z. Jr.: Combining convolutional side-outputs for road image segmentation. 2019 International Joint Conference on Neural Networks (IJCNN) - \textbfAccepted 2019.
44	RoadNet-RT			93.98 %	95.19 %	94.47 %	93.49 %	6.01 %	6.51 %	8m s	GPU @ 2.5 Ghz (Python)
L. Bai, Y. Lyu and X. Huang: RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation. arXiv preprint arXiv:2006.07644 2020.
45	RGB36-Super			93.90 %	94.39 %	94.70 %	93.11 %	5.73 %	6.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Caltagirone, S. Lennart, M. Wahde and M. Sanfridson: Lidar-Camera Co-Training for Semi- Supervised Road Detection. arXiv preprint arXiv:1911.12597 2019.
46	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.
47	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.
48	LidarHisto		code	93.32 %	93.19 %	95.39 %	91.34 %	4.85 %	8.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Chen, J. Yang and H. Kong: Lidar-histogram for fast road and obstacle detection. 2017 IEEE International Conference on Robotics and Automation (ICRA) 2017.
49	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.
50	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.
51	MixedCRF			92.75 %	90.24 %	94.03 %	91.50 %	6.39 %	8.50 %	6s	1 core @ 2.5 Ghz (Matlab + C/C++)
X. Han, H. Wang, J. Lu and C. Zhao: Road detection based on the fusion of Lidar and image data. 2017.
52	BMCF			92.21 %	87.99 %	91.55 %	92.89 %	9.43 %	7.11 %	2.5 s	1 core @ 2.5 Ghz (C/C++)
L. Wang, T. Wu, Z. Xiao, L. Xiao, D. Zhao and J. Han: Multi-cue road boundary detection using stereo vision. 2016 IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2016.
53	HybridCRF			91.95 %	86.44 %	94.01 %	89.98 %	6.30 %	10.02 %	1.5 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, R. Wang, B. Dai, Y. Fang, D. Liu and T. Wu: Hybrid conditional random field based camera-LIDAR fusion for road detection. Information Sciences 2018.
54	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.
55	ProbBoost			91.36 %	84.92 %	88.18 %	94.78 %	13.97 %	5.22 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
G. Vitor, A. Victorino and J. Ferreira: A probabilistic distribution approach for the classification of urban roads in complex environments. Workshop on Modelling, Estimation, Perception and Control of All Terrain Mobile Robots on IEEE International Conference on Robotics and Automation (ICRA) 2014.
56	NNP			91.34 %	88.65 %	91.07 %	91.60 %	9.87 %	8.40 %	5 s	4 cores @ 2.5 Ghz (Matlab)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
57	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.
58	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.
59	RES3D-Velo			90.60 %	85.38 %	85.96 %	95.78 %	17.20 %	4.22 %	0.36 s	1 core @ 2.5 Ghz (C/C++)
P. Shinzato, D. Wolf and C. Stiller: Road Terrain Detection: Avoiding Common Obstacle Detection Assumptions Using Sensor Fusion. Intelligent Vehicles Symposium (IV) 2014.
60	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.
61	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.
62	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.
63	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.
64	FusedCRF			89.51 %	83.53 %	86.64 %	92.58 %	15.69 %	7.42 %	2 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, B. Dai, D. Liu, T. Hu and T. Wu: CRF based Road Detection with Multi-Sensor Fusion. Intelligent Vehicles Symposium (IV) 2015.
65	BM			89.41 %	80.61 %	83.43 %	96.30 %	21.02 %	3.70 %	2 s	2 cores @ 2.5 Ghz (Matlab)
B. WANG, V. Fremont and S. Rodriguez Florez: Color-based Road Detection and its Evaluation on the KITTI Road Benchmark. Workshop on Benchmarking Road Terrain and Lane Detection Algorithms for In-Vehicle Application, IEEE Intelligent Vehicles Symposium 2014.
66	HistonBoost			88.73 %	81.57 %	84.49 %	93.42 %	18.85 %	6.58 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
G. Vitor, A. Victorino and J. Ferreira: Comprehensive Performance Analysis of Road Detection Algorithms Using the Common Urban Kitti-Road Benchmark. Workshop on Benchmarking Road Terrain and Lane Detection Algorithms for In-Vehicle Application on IEEE Intelligent Vehicles Symposium (IV) 2014.
67	geo+gpr+crf			88.20 %	82.33 %	85.32 %	91.27 %	17.26 %	8.73 %	30 s	1 core @ 2.0 Ghz (C/C++)
Z. Xiao, B. Dai, H. Li, T. Wu, X. Xu, Y. Zeng and T. Chen: Gaussian process regression-based robust free space detection for autonomous vehicle by 3-D point cloud and 2-D appearance information fusion. International Journal of Advanced Robotic Systems 2017.
68	GRES3D+VELO			88.19 %	88.65 %	83.98 %	92.85 %	19.48 %	7.15 %	60 ms	4 cores @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
69	SCRFFPFHGSP			87.96 %	83.16 %	90.01 %	86.01 %	10.50 %	13.99 %	5 s	8 cores @ 2.5 Ghz (C/C++, Matlab)
I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.
70	GRES3D+SELAS			87.57 %	90.52 %	85.92 %	89.28 %	16.08 %	10.72 %	110 ms	4 core @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
71	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.
72	RES3D-Stereo			83.62 %	85.74 %	79.81 %	87.81 %	24.42 %	12.19 %	0.7 s	1 core @ 2.5 Ghz (C/C++)
P. Shinzato, D. Gomes and D. Wolf: Road estimation with sparse 3D points from stereo data. Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on 2014.
73	SPlane			82.28 %	82.83 %	76.85 %	88.53 %	29.32 %	11.47 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
74	SPlane + BL			82.04 %	85.56 %	75.11 %	90.39 %	32.93 %	9.61 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
75	ANN			80.95 %	68.36 %	69.95 %	96.05 %	45.35 %	3.95 %	3 s	1 core @ 3.0 Ghz (C/C++)
G. Vitor, D. Lima, A. Victorino and J. Ferreira: A 2D/3D Vision Based Approach Applied to Road Detection in Urban Environments. Intelligent Vehicles Symposium (IV), 2013 IEEE 2013.

	Method	Setting	Code	MaxF	AP	PRE	REC	FPR	FNR	Runtime	Environment
1	SNE-RoadSegV2			97.08 %	92.87 %	96.83 %	97.34 %	1.04 %	2.66 %	0.03 s	GPU @ 2.5 Ghz (Python)
Y. Feng, Y. Ma, Q. Chen, I. Pitas and R. Fan: SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection. 2024.
2	SNE-RoadSeg+			97.04 %	92.97 %	96.84 %	97.24 %	1.03 %	2.76 %	0.08 s	GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan, P. Cai and M. Liu: SNE-RoadSeg+: Rethinking depth-normal translation and deep supervision for freespace detection. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021.
3	RoadFormer+			97.02 %	92.33 %	96.75 %	97.29 %	1.06 %	2.71 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

4	RoadFormer			97.02 %	92.78 %	96.61 %	97.43 %	1.12 %	2.57 %	0.07 s	GPU @ 2.5 Ghz (Python)
J. Li, Y. Zhang, P. Yun, G. Zhou, Q. Chen and R. Fan: RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing. IEEE Transactions on Intelligent Vehicles 2024.
5	PLB-RD			96.93 %	93.08 %	96.78 %	97.09 %	1.05 %	2.91 %	0.46 s	GPU @ 2.5 Ghz (Python)
L. Sun, H. Zhang and W. Yin: Pseudo-LiDAR-Based Road Detection. IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT) 2022.
6	CLCFNet-SD			96.57 %	89.77 %	96.29 %	96.85 %	1.22 %	3.15 %	0.05 s	GPU @ 2.5 Ghz (Python)

7	Epurate-Net			96.44 %	91.85 %	95.89 %	97.00 %	1.36 %	3.00 %	0.02 s	GPU @ 2.5 Ghz (Python)

8	DFM-RTFNet			96.26 %	93.01 %	96.16 %	96.35 %	1.25 %	3.65 %	0.08 s	GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan, Y. Sun and M. Liu: Dynamic fusion module evolves drivable area and road anomaly detection: A benchmark and algorithms. IEEE Transactions on Cybernetics 2021.
9	LRDNet+		code	96.18 %	90.03 %	95.94 %	96.42 %	1.33 %	3.58 %	0.01 s	GPU @ 2.5 Ghz (Python)
A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.
10	USNet		code	96.11 %	91.71 %	95.86 %	96.37 %	1.36 %	3.63 %	0.02 s	GPU @ 1.5 Ghz (Python)
Y. Chang, F. Xue, F. Sheng, W. Liang and A. Ming: Fast Road Segmentation via Uncertainty-aware Symmetric Network. IEEE International Conference on Robotics and Automation (ICRA) 2022.
11	LRDNet (L)		code	96.10 %	89.59 %	95.97 %	96.22 %	1.32 %	3.78 %	0.1 s	GPU @ 2.5 Ghz (Python)
A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.
12	SNE-RoadSeg		code	96.03 %	93.03 %	96.22 %	95.83 %	1.23 %	4.17 %	0.18 s	GPU @ 2.5 Ghz (Python)
R. Fan, H. Wang, P. Cai and M. Liu: SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection. ECCV 2020.
13	PLARD		code	95.95 %	95.25 %	96.25 %	95.65 %	1.21 %	4.35 %	0.16 s	GPU @ 2.5 Ghz (Python)
Z. Chen, J. Zhang and D. Tao: Progressive LiDAR adaptation for road detection. IEEE/CAA Journal of Automatica Sinica 2019.
14	LRDNet(S)		code	95.78 %	90.75 %	95.62 %	95.95 %	1.43 %	4.05 %	.009 s	GPU @ 2.5 Ghz (Python)
A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.
15	CLCFNet			95.68 %	88.37 %	94.75 %	96.63 %	1.75 %	3.37 %	0.02 s	GPU @ 1.5 Ghz (Python)
S. Gu, J. Yang and H. Kong: A Cascaded LiDAR-Camera Fusion Network for Road Detection. ICRA 2021.
16	CLCFNet-SD (LiDAR)			95.50 %	88.06 %	94.40 %	96.63 %	1.87 %	3.37 %	0.04 s	GPU @ 2.5 Ghz (Python)

17	CLCFNet (LiDAR)			95.25 %	88.02 %	94.36 %	96.16 %	1.87 %	3.84 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
S. Gu, J. Yang and H. Kong: A Cascaded LiDAR-Camera Fusion Network for Road Detection. ICRA 2021.
18	NIM-RTFNet			95.11 %	92.94 %	95.91 %	94.32 %	1.31 %	5.68 %	0.05 s	GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan, Y. Sun and M. Liu: Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
19	RBANet			94.91 %	86.35 %	92.53 %	97.42 %	2.56 %	2.58 %	0.16 s	GPU @ 1.5 Ghz (Python + C/C++)
J. Sun, S. Kim, S. Lee, Y. Kim and S. Ko: Reverse and Boundary Attention Network for Road Segmentation. Proceedings of the IEEE International Conference on Computer Vision Workshops 2019.
20	LidCamNet			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. Robotics and Autonomous Systems 2018.
21	RGB36-Cotrain			94.53 %	92.54 %	94.60 %	94.46 %	1.76 %	5.54 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Caltagirone, S. Lennart, M. Wahde and M. Sanfridson: Lidar-Camera Co-Training for Semi- Supervised Road Detection. arXiv preprint arXiv:1911.12597 2019.
22	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.
23	LC-CRF			94.01 %	85.24 %	91.31 %	96.88 %	3.00 %	3.12 %	0.18 s	GPU @ 1.5 Ghz (Python + C/C++)
S. Gu, Y. Zhang, J. Tang, J. Yang and H. Kong: Road Detection through CRF based LiDAR-Camera Fusion. ICRA 2019.
24	MultiNet		code	93.69 %	92.55 %	94.24 %	93.14 %	1.85 %	6.86 %	0.17 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Teichmann, M. Weber, J. Zoellner, R. Cipolla and R. Urtasun: MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving. CoRR 2016.
25	TVFNet			93.65 %	87.57 %	93.87 %	93.43 %	1.99 %	6.57 %	0.04 s	GPU @ 1.5 Ghz (Python)
S. Gu, Y. Zhang, J. Yang, J. Alvarez and H. Kong: Two-View Fusion based Convolutional Neural Network for Urban Road Detection. IROS 2019.
26	LFD-RoadSeg		code	93.49 %	92.19 %	93.46 %	93.52 %	2.13 %	6.48 %	.004 s	GPU @ 1.5 Ghz (Python)
H. Zhou, F. Xue, Y. Li, S. Gong, Y. Li and Y. Zhou: Exploiting Low-Level Representations for Ultra-Fast Road Segmentation. IEEE Transactions on Intelligent Transportation Systems 2024.
27	StixelNet II			93.40 %	85.01 %	91.05 %	95.87 %	3.07 %	4.13 %	1.2 s	1 core @ 3.0 Ghz (Matlab + C/C++)
N. Garnett, S. Silberstein, S. Oron, E. Fetaya, U. Verner, A. Ayash, V. Goldner, R. Cohen, K. Horn and D. Levi: Real-time category-based and general obstacle detection for autonomous driving. 5th Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving (CVRSUAD'17, IEEE-ICCV 2017 Workshop) 2017.
28	HA-DeepLabv3+			93.24 %	91.83 %	93.19 %	93.28 %	2.22 %	6.72 %	0.06 s	1 core @ 2.5 Ghz (Python)
R. Fan, H. Wang, P. Cai, J. Wu, M. Bocus, L. Qiao and M. Liu: Learning collision-free space detection from stereo images: Homography matrix brings better data augmentation. IEEE/ASME Transactions on Mechatronics 2021.
29	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.
30	Hadamard-FCN			93.14 %	90.00 %	93.31 %	92.98 %	2.17 %	7.02 %	0.02 s	GPU @ 1.5 Ghz (Python)
M. Oeljeklaus: An Integrated Approach for Traffic Scene Understanding from Monocular Cameras. 2021.
31	BJN			93.13 %	87.59 %	93.89 %	92.38 %	1.96 %	7.62 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
B. Yu, D. Lee, J. Lee and S. Kee: Free Space Detection Using Camera-LiDAR Fusion in a Bird’s Eye View Plane. Sensors 2021.
32	RoadNet3			92.95 %	91.93 %	93.32 %	92.58 %	2.16 %	7.42 %	300 ms	GPU @ GTX950M (Python +Tensorflow)
Y. Lyu, L. Bai and X. Huang: Road Segmentation using CNN and Distributed LSTM. 2019 IEEE International Symposium on Circuits and Systems (ISCAS) 2019.
33	ChipNet			92.91 %	84.95 %	90.98 %	94.91 %	3.06 %	5.09 %	12 ms	GPU @ 1.5 Ghz (Keras)
Y. Lyu, L. Bai and X. Huang: ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA. IEEE Transactions on Circuits and Systems I: Regular Papers 2019.
34	TEDNet			92.78 %	91.08 %	91.86 %	93.71 %	2.71 %	6.29 %	0.09 s	GPU @ 2.5 Ghz (Python)
M. Bayón-Gutiérrez, J. Benítez- Andrades, S. Rubio-Martín, J. Aveleira-Mata, H. Alaiz-Moretón and M. García-Ordás: Roadway Detection Using Convolutional Neural Network Through Camera and LiDAR Data. Hybrid Artificial Intelligent Systems 2022.
35	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.
36	OFA Net		code	92.62 %	83.12 %	88.97 %	96.58 %	3.90 %	3.42 %	0.04 s	GPU @ 1.5 Ghz (Python)
S. Zhang, Z. Zhang, L. Sun and W. Qin: One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation. Applied Sciences 2019.
37	CLRD			92.41 %	90.16 %	92.30 %	92.52 %	2.52 %	7.48 %	0.05 s	GPU @ 2.5 Ghz (Python)
M. Bayón-Gutiérrez, J. Benítez- Andrades, S. Rubio-Martín, J. Aveleira-Mata, H. Alaiz-Moretón and M. García-Ordás: Roadway Detection Using Convolutional Neural Network Through Camera and LiDAR Data. Hybrid Artificial Intelligent Systems 2022.
38	LoDNN			92.29 %	90.35 %	90.81 %	93.81 %	3.09 %	6.19 %	18 ms	GPU @ 2.5 Ghz (Torch)
L. Caltagirone, S. Scheidegger, L. Svensson and M. Wahde: Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks. IEEE Intelligent Vehicles Symposium 2017.
39	Up-Conv			91.89 %	89.44 %	92.59 %	91.20 %	2.38 %	8.80 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.
40	DDN			91.76 %	86.84 %	93.06 %	90.50 %	2.20 %	9.50 %	2 s	GPU @ 2.5 Ghz (Python + C/C++)
R. Mohan: Deep Deconvolutional Networks for Scene Parsing. 2014.
41	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.
42	RGB36-Super			91.15 %	90.16 %	89.68 %	92.68 %	3.48 %	7.32 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Caltagirone, S. Lennart, M. Wahde and M. Sanfridson: Lidar-Camera Co-Training for Semi- Supervised Road Detection. arXiv preprint arXiv:1911.12597 2019.
43	RoadNet-RT			90.79 %	91.67 %	91.79 %	89.80 %	2.62 %	10.20 %	8m s	GPU @ 2.5 Ghz (Python)
L. Bai, Y. Lyu and X. Huang: RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation. arXiv preprint arXiv:2006.07644 2020.
44	HID-LS			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 2019.
45	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.
46	ALO-AVG-MM		code	89.45 %	79.87 %	85.40 %	93.90 %	5.23 %	6.10 %	0.0296 sec	GeForce GTX 1080 GPU (Python)
F. Reis, R. Almeida, E. Kijak, S. Malinowski, S. Guimaraes and Z. Jr.: Combining convolutional side-outputs for road image segmentation. 2019 International Joint Conference on Neural Networks (IJCNN) - \textbfAccepted 2019.
47	HybridCRF			88.53 %	80.79 %	86.41 %	90.76 %	4.65 %	9.24 %	1.5 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, R. Wang, B. Dai, Y. Fang, D. Liu and T. Wu: Hybrid conditional random field based camera-LIDAR fusion for road detection. Information Sciences 2018.
48	LidarHisto		code	86.55 %	81.13 %	90.71 %	82.75 %	2.76 %	17.25 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Chen, J. Yang and H. Kong: Lidar-histogram for fast road and obstacle detection. 2017 IEEE International Conference on Robotics and Automation (ICRA) 2017.
49	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.
50	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.
51	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.
52	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.
53	MixedCRF			85.69 %	75.12 %	80.17 %	92.02 %	7.42 %	7.98 %	6s	1 core @ 2.5 Ghz (Matlab + C/C++)
X. Han, H. Wang, J. Lu and C. Zhao: Road detection based on the fusion of Lidar and image data. 2017.
54	NNP			85.55 %	76.90 %	85.36 %	85.75 %	4.79 %	14.25 %	5 s	4 cores @ 2.5 Ghz (Matlab)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
55	BMCF			85.46 %	74.07 %	85.06 %	85.86 %	4.91 %	14.14 %	2.5 s	1 core @ 2.5 Ghz (C/C++)
L. Wang, T. Wu, Z. Xiao, L. Xiao, D. Zhao and J. Han: Multi-cue road boundary detection using stereo vision. 2016 IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2016.
56	FusedCRF			84.49 %	72.35 %	77.13 %	93.40 %	9.02 %	6.60 %	2 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, B. Dai, D. Liu, T. Hu and T. Wu: CRF based Road Detection with Multi-Sensor Fusion. Intelligent Vehicles Symposium (IV) 2015.
57	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.
58	GRES3D+VELO			84.14 %	80.20 %	80.57 %	88.03 %	6.92 %	11.97 %	60 ms	4 cores @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
59	RES3D-Velo			83.63 %	72.58 %	77.38 %	90.97 %	8.67 %	9.03 %	0.36 s	1 core @ 2.5 Ghz (C/C++)
P. Shinzato, D. Wolf and C. Stiller: Road Terrain Detection: Avoiding Common Obstacle Detection Assumptions Using Sensor Fusion. Intelligent Vehicles Symposium (IV) 2014.
60	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.
61	GRES3D+SELAS			82.70 %	83.95 %	78.54 %	87.32 %	7.77 %	12.68 %	110 ms	4 core @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
62	geo+gpr+crf			81.00 %	69.74 %	79.78 %	82.27 %	6.79 %	17.73 %	30 s	1 core @ 2.0 Ghz (C/C++)
Z. Xiao, B. Dai, H. Li, T. Wu, X. Xu, Y. Zeng and T. Chen: Gaussian process regression-based robust free space detection for autonomous vehicle by 3-D point cloud and 2-D appearance information fusion. International Journal of Advanced Robotic Systems 2017.
63	SCRFFPFHGSP			80.78 %	70.80 %	81.07 %	80.50 %	6.13 %	19.50 %	5 s	8 cores @ 2.5 Ghz (C/C++, Matlab)
I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.
64	ProbBoost			80.76 %	68.70 %	85.25 %	76.72 %	4.33 %	23.28 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
G. Vitor, A. Victorino and J. Ferreira: A probabilistic distribution approach for the classification of urban roads in complex environments. Workshop on Modelling, Estimation, Perception and Control of All Terrain Mobile Robots on IEEE International Conference on Robotics and Automation (ICRA) 2014.
65	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.
66	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.
67	RES3D-Stereo			78.75 %	73.60 %	77.63 %	79.90 %	7.50 %	20.10 %	0.7 s	1 core @ 2.5 Ghz (C/C++)
P. Shinzato, D. Gomes and D. Wolf: Road estimation with sparse 3D points from stereo data. Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on 2014.
68	BM			78.43 %	62.46 %	70.87 %	87.80 %	11.76 %	12.20 %	2 s	2 cores @ 2.5 Ghz (Matlab)
B. WANG, V. Fremont and S. Rodriguez Florez: Color-based Road Detection and its Evaluation on the KITTI Road Benchmark. Workshop on Benchmarking Road Terrain and Lane Detection Algorithms for In-Vehicle Application, IEEE Intelligent Vehicles Symposium 2014.
69	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.
70	HistonBoost			74.19 %	63.01 %	77.43 %	71.22 %	6.77 %	28.78 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
G. Vitor, A. Victorino and J. Ferreira: Comprehensive Performance Analysis of Road Detection Algorithms Using the Common Urban Kitti-Road Benchmark. Workshop on Benchmarking Road Terrain and Lane Detection Algorithms for In-Vehicle Application on IEEE Intelligent Vehicles Symposium (IV) 2014.
71	SPlane + BL			74.02 %	79.61 %	65.15 %	85.68 %	14.93 %	14.32 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
72	SPlane			73.30 %	69.11 %	65.39 %	83.38 %	14.38 %	16.62 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
73	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.
74	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.
75	ANN			54.07 %	36.61 %	39.28 %	86.69 %	43.67 %	13.31 %	3 s	1 core @ 3.0 Ghz (C/C++)
G. Vitor, D. Lima, A. Victorino and J. Ferreira: A 2D/3D Vision Based Approach Applied to Road Detection in Urban Environments. Intelligent Vehicles Symposium (IV), 2013 IEEE 2013.

	Method	Setting	Code	MaxF	AP	PRE	REC	FPR	FNR	Runtime	Environment
1	RoadFormer+			97.56 %	93.74 %	97.43 %	97.69 %	1.42 %	2.31 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

2	SNE-RoadSegV2			97.55 %	93.98 %	97.57 %	97.53 %	1.34 %	2.47 %	0.03 s	GPU @ 2.5 Ghz (Python)
Y. Feng, Y. Ma, Q. Chen, I. Pitas and R. Fan: SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection. 2024.
3	RoadFormer			97.50 %	93.85 %	97.16 %	97.84 %	1.57 %	2.16 %	0.07 s	GPU @ 2.5 Ghz (Python)
J. Li, Y. Zhang, P. Yun, G. Zhou, Q. Chen and R. Fan: RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing. IEEE Transactions on Intelligent Vehicles 2024.
4	SNE-RoadSeg+			97.50 %	93.98 %	97.41 %	97.58 %	1.43 %	2.42 %	0.08 s	GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan, P. Cai and M. Liu: SNE-RoadSeg+: Rethinking depth-normal translation and deep supervision for freespace detection. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021.
5	PLB-RD			97.42 %	94.09 %	97.30 %	97.54 %	1.49 %	2.46 %	0.46 s	GPU @ 2.5 Ghz (Python)
L. Sun, H. Zhang and W. Yin: Pseudo-LiDAR-Based Road Detection. IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT) 2022.
6	CLCFNet-SD			97.20 %	91.55 %	97.15 %	97.25 %	1.57 %	2.75 %	0.05 s	GPU @ 2.5 Ghz (Python)

7	Epurate-Net			97.09 %	93.08 %	96.76 %	97.43 %	1.80 %	2.57 %	0.02 s	GPU @ 2.5 Ghz (Python)

8	PLARD		code	97.03 %	94.03 %	97.19 %	96.88 %	1.54 %	3.12 %	0.16 s	GPU @ 2.5 Ghz (Python)
Z. Chen, J. Zhang and D. Tao: Progressive LiDAR adaptation for road detection. IEEE/CAA Journal of Automatica Sinica 2019.
9	LRDNet+		code	96.95 %	92.22 %	96.88 %	97.02 %	1.72 %	2.98 %	0.01 s	GPU @ 2.5 Ghz (Python)
A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.
10	USNet		code	96.89 %	93.25 %	96.51 %	97.27 %	1.94 %	2.73 %	0.02 s	GPU @ 1.5 Ghz (Python)
Y. Chang, F. Xue, F. Sheng, W. Liang and A. Ming: Fast Road Segmentation via Uncertainty-aware Symmetric Network. IEEE International Conference on Robotics and Automation (ICRA) 2022.
11	LRDNet (L)		code	96.87 %	91.91 %	96.73 %	97.01 %	1.81 %	2.99 %	0.1 s	GPU @ 2.5 Ghz (Python)
A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.
12	DFM-RTFNet			96.78 %	94.05 %	96.62 %	96.93 %	1.87 %	3.07 %	0.08 s	GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan, Y. Sun and M. Liu: Dynamic fusion module evolves drivable area and road anomaly detection: A benchmark and algorithms. IEEE Transactions on Cybernetics 2021.
13	SNE-RoadSeg		code	96.75 %	94.07 %	96.90 %	96.61 %	1.70 %	3.39 %	0.18 s	GPU @ 2.5 Ghz (Python)
R. Fan, H. Wang, P. Cai and M. Liu: SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection. ECCV 2020.
14	LRDNet(S)		code	96.74 %	92.54 %	96.79 %	96.69 %	1.76 %	3.31 %	.009 s	GPU @ 2.5 Ghz (Python)
A. Khan, S. Jie, R. Yunbo, Lei and H. Shen: LRDNet: Lightweight LiDAR Aided Cascaded Feature Pools for Free Road Space Detection. IEEE Transactions on Multimedia 2023.
15	CLCFNet			96.38 %	90.85 %	96.38 %	96.39 %	1.99 %	3.61 %	0.02 s	GPU @ 1.5 Ghz (Python)
S. Gu, J. Yang and H. Kong: A Cascaded LiDAR-Camera Fusion Network for Road Detection. ICRA 2021.
16	RBANet			96.30 %	89.72 %	95.14 %	97.50 %	2.75 %	2.50 %	0.16 s	GPU @ 1.5 Ghz (Python + C/C++)
J. Sun, S. Kim, S. Lee, Y. Kim and S. Ko: Reverse and Boundary Attention Network for Road Segmentation. Proceedings of the IEEE International Conference on Computer Vision Workshops 2019.
17	CLCFNet-SD (LiDAR)			96.30 %	90.66 %	96.17 %	96.43 %	2.12 %	3.57 %	0.04 s	GPU @ 2.5 Ghz (Python)

18	LidCamNet			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. Robotics and Autonomous Systems 2018.
19	NIM-RTFNet			96.02 %	94.01 %	96.43 %	95.62 %	1.95 %	4.38 %	0.05 s	GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan, Y. Sun and M. Liu: Applying Surface Normal Information in Drivable Area and Road Anomaly Detection for Ground Mobile Robots. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
20	CLCFNet (LiDAR)			95.97 %	90.61 %	96.12 %	95.82 %	2.13 %	4.18 %	0.02 s	GPU @ 1.5 Ghz (C/C++)
S. Gu, J. Yang and H. Kong: A Cascaded LiDAR-Camera Fusion Network for Road Detection. ICRA 2021.
21	LC-CRF			95.68 %	88.34 %	93.62 %	97.83 %	3.67 %	2.17 %	0.18 s	GPU @ 1.5 Ghz (Python + C/C++)
S. Gu, Y. Zhang, J. Tang, J. Yang and H. Kong: Road Detection through CRF based LiDAR-Camera Fusion. ICRA 2019.
22	RGB36-Cotrain			95.55 %	93.71 %	95.68 %	95.42 %	2.37 %	4.58 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Caltagirone, S. Lennart, M. Wahde and M. Sanfridson: Lidar-Camera Co-Training for Semi- Supervised Road Detection. arXiv preprint arXiv:1911.12597 2019.
23	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.
24	TVFNet			95.34 %	90.26 %	95.73 %	94.94 %	2.33 %	5.06 %	0.04 s	GPU @ 1.5 Ghz (Python)
S. Gu, Y. Zhang, J. Yang, J. Alvarez and H. Kong: Two-View Fusion based Convolutional Neural Network for Urban Road Detection. IROS 2019.
25	LFD-RoadSeg		code	95.21 %	93.71 %	95.35 %	95.08 %	2.56 %	4.92 %	.004 s	GPU @ 1.5 Ghz (Python)
H. Zhou, F. Xue, Y. Li, S. Gong, Y. Li and Y. Zhou: Exploiting Low-Level Representations for Ultra-Fast Road Segmentation. IEEE Transactions on Intelligent Transportation Systems 2024.
26	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.
27	BJN			94.89 %	90.63 %	96.14 %	93.67 %	2.07 %	6.33 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
B. Yu, D. Lee, J. Lee and S. Kee: Free Space Detection Using Camera-LiDAR Fusion in a Bird’s Eye View Plane. Sensors 2021.
28	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.
29	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.
30	Hadamard-FCN			94.85 %	91.48 %	94.81 %	94.89 %	2.86 %	5.11 %	0.02 s	GPU @ 1.5 Ghz (Python)
M. Oeljeklaus: An Integrated Approach for Traffic Scene Understanding from Monocular Cameras. 2021.
31	HA-DeepLabv3+			94.83 %	93.24 %	94.77 %	94.89 %	2.88 %	5.11 %	0.06 s	1 core @ 2.5 Ghz (Python)
R. Fan, H. Wang, P. Cai, J. Wu, M. Bocus, L. Qiao and M. Liu: Learning collision-free space detection from stereo images: Homography matrix brings better data augmentation. IEEE/ASME Transactions on Mechatronics 2021.
32	TEDNet			94.62 %	93.05 %	94.28 %	94.96 %	3.17 %	5.04 %	0.09 s	GPU @ 2.5 Ghz (Python)
M. Bayón-Gutiérrez, J. Benítez- Andrades, S. Rubio-Martín, J. Aveleira-Mata, H. Alaiz-Moretón and M. García-Ordás: Roadway Detection Using Convolutional Neural Network Through Camera and LiDAR Data. Hybrid Artificial Intelligent Systems 2022.
33	RoadNet3			94.44 %	93.45 %	94.69 %	94.18 %	2.91 %	5.82 %	300 ms	GPU @ GTX950M (Python +Tensorflow)
Y. Lyu, L. Bai and X. Huang: Road Segmentation using CNN and Distributed LSTM. 2019 IEEE International Symposium on Circuits and Systems (ISCAS) 2019.
34	CLRD			94.20 %	92.66 %	94.25 %	94.14 %	3.16 %	5.86 %	0.05 s	GPU @ 2.5 Ghz (Python)
M. Bayón-Gutiérrez, J. Benítez- Andrades, S. Rubio-Martín, J. Aveleira-Mata, H. Alaiz-Moretón and M. García-Ordás: Roadway Detection Using Convolutional Neural Network Through Camera and LiDAR Data. Hybrid Artificial Intelligent Systems 2022.
35	LoDNN			94.07 %	92.03 %	92.81 %	95.37 %	4.07 %	4.63 %	18 ms	GPU @ 2.5 Ghz (Torch)
L. Caltagirone, S. Scheidegger, L. Svensson and M. Wahde: Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks. IEEE Intelligent Vehicles Symposium 2017.
36	ChipNet			94.05 %	88.29 %	93.57 %	94.53 %	3.58 %	5.47 %	12 ms	GPU @ 1.5 Ghz (Keras)
Y. Lyu, L. Bai and X. Huang: ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA. IEEE Transactions on Circuits and Systems I: Regular Papers 2019.
37	DEEP-DIG			93.98 %	93.65 %	94.26 %	93.69 %	3.14 %	6.31 %	0.14 s	GPU @ 3.5 Ghz (Python + C/C++)
J. Muñoz-Bulnes, C. Fernandez, I. Parra, D. Fernández-Llorca and M. Sotelo: Deep Fully Convolutional Networks with Random Data Augmentation for Enhanced Generalization in Road Detection. Workshop on Deep Learning for Autonomous Driving on IEEE 20th International Conference on Intelligent Transportation Systems 2017.
38	Up-Conv-Poly		code	93.83 %	90.47 %	94.00 %	93.67 %	3.29 %	6.33 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.
39	OFA Net		code	93.74 %	85.37 %	90.36 %	97.38 %	5.72 %	2.62 %	0.04 s	GPU @ 1.5 Ghz (Python)
S. Zhang, Z. Zhang, L. Sun and W. Qin: One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation. Applied Sciences 2019.
40	DDN			93.43 %	89.67 %	95.09 %	91.82 %	2.61 %	8.18 %	2 s	GPU @ 2.5 Ghz (Python + C/C++)
R. Mohan: Deep Deconvolutional Networks for Scene Parsing. 2014.
41	HID-LS			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 2019.
42	RGB36-Super			92.94 %	92.29 %	93.14 %	92.74 %	3.77 %	7.26 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Caltagirone, S. Lennart, M. Wahde and M. Sanfridson: Lidar-Camera Co-Training for Semi- Supervised Road Detection. arXiv preprint arXiv:1911.12597 2019.
43	RoadNet-RT			92.55 %	93.21 %	92.94 %	92.16 %	3.86 %	7.84 %	8m s	GPU @ 2.5 Ghz (Python)
L. Bai, Y. Lyu and X. Huang: RoadNet-RT: High Throughput CNN Architecture and SoC Design for Real-Time Road Segmentation. arXiv preprint arXiv:2006.07644 2020.
44	Up-Conv			92.39 %	90.24 %	93.03 %	91.76 %	3.79 %	8.24 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
G. Oliveira, W. Burgard and T. Brox: Efficient Deep Methods for Monocular Road Segmentation. IROS 2016.
45	ALO-AVG-MM		code	92.03 %	85.64 %	90.65 %	93.45 %	5.31 %	6.55 %	0.0296 sec	GeForce GTX 1080 GPU (Python)
F. Reis, R. Almeida, E. Kijak, S. Malinowski, S. Guimaraes and Z. Jr.: Combining convolutional side-outputs for road image segmentation. 2019 International Joint Conference on Neural Networks (IJCNN) - \textbfAccepted 2019.
46	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.
47	HybridCRF			90.81 %	86.01 %	91.05 %	90.57 %	4.90 %	9.43 %	1.5 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, R. Wang, B. Dai, Y. Fang, D. Liu and T. Wu: Hybrid conditional random field based camera-LIDAR fusion for road detection. Information Sciences 2018.
48	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.
49	LidarHisto		code	90.67 %	84.79 %	93.06 %	88.41 %	3.63 %	11.59 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Chen, J. Yang and H. Kong: Lidar-histogram for fast road and obstacle detection. 2017 IEEE International Conference on Robotics and Automation (ICRA) 2017.
50	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.
51	MixedCRF			90.59 %	84.24 %	89.11 %	92.13 %	6.20 %	7.87 %	6s	1 core @ 2.5 Ghz (Matlab + C/C++)
X. Han, H. Wang, J. Lu and C. Zhao: Road detection based on the fusion of Lidar and image data. 2017.
52	BMCF			89.75 %	84.15 %	89.02 %	90.49 %	6.15 %	9.51 %	2.5 s	1 core @ 2.5 Ghz (C/C++)
L. Wang, T. Wu, Z. Xiao, L. Xiao, D. Zhao and J. Han: Multi-cue road boundary detection using stereo vision. 2016 IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2016.
53	NNP			89.68 %	86.50 %	89.67 %	89.68 %	5.69 %	10.32 %	5 s	4 cores @ 2.5 Ghz (Matlab)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
54	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.
55	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.
56	FusedCRF			88.25 %	79.24 %	83.62 %	93.44 %	10.08 %	6.56 %	2 s	1 core @ 2.5 Ghz (C/C++)
L. Xiao, B. Dai, D. Liu, T. Hu and T. Wu: CRF based Road Detection with Multi-Sensor Fusion. Intelligent Vehicles Symposium (IV) 2015.
57	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.
58	ProbBoost			87.78 %	77.30 %	86.59 %	89.01 %	7.60 %	10.99 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
G. Vitor, A. Victorino and J. Ferreira: A probabilistic distribution approach for the classification of urban roads in complex environments. Workshop on Modelling, Estimation, Perception and Control of All Terrain Mobile Robots on IEEE International Conference on Robotics and Automation (ICRA) 2014.
59	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.
60	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.
61	RES3D-Velo			86.58 %	78.34 %	82.63 %	90.92 %	10.53 %	9.08 %	0.36 s	1 core @ 2.5 Ghz (C/C++)
P. Shinzato, D. Wolf and C. Stiller: Road Terrain Detection: Avoiding Common Obstacle Detection Assumptions Using Sensor Fusion. Intelligent Vehicles Symposium (IV) 2014.
62	GRES3D+VELO			86.07 %	84.34 %	82.16 %	90.38 %	10.81 %	9.62 %	60 ms	4 cores @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
63	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.
64	geo+gpr+crf			85.56 %	74.21 %	82.81 %	88.50 %	10.12 %	11.50 %	30 s	1 core @ 2.0 Ghz (C/C++)
Z. Xiao, B. Dai, H. Li, T. Wu, X. Xu, Y. Zeng and T. Chen: Gaussian process regression-based robust free space detection for autonomous vehicle by 3-D point cloud and 2-D appearance information fusion. International Journal of Advanced Robotic Systems 2017.
65	GRES3D+SELAS			85.09 %	86.86 %	82.27 %	88.10 %	10.46 %	11.90 %	110 ms	4 core @ 2.8 Ghz (C/C++)
P. Shinzato: Estimation of obstacles and road area with sparse 3D points. 2015.
66	SCRFFPFHGSP			84.93 %	76.31 %	85.37 %	84.49 %	7.98 %	15.51 %	5 s	8 cores @ 2.5 Ghz (C/C++, Matlab)
I. Gheorghe: Semantic Segmentation of Terrain and Road Terrain for Advanced Driver Assistance Systems. 2015.
67	HistonBoost			83.92 %	73.75 %	82.24 %	85.66 %	10.19 %	14.34 %	2.5 min	>8 cores @ 3.0 Ghz (C/C++)
G. Vitor, A. Victorino and J. Ferreira: Comprehensive Performance Analysis of Road Detection Algorithms Using the Common Urban Kitti-Road Benchmark. Workshop on Benchmarking Road Terrain and Lane Detection Algorithms for In-Vehicle Application on IEEE Intelligent Vehicles Symposium (IV) 2014.
68	BM			83.47 %	72.23 %	75.90 %	92.72 %	16.22 %	7.28 %	2 s	2 cores @ 2.5 Ghz (Matlab)
B. WANG, V. Fremont and S. Rodriguez Florez: Color-based Road Detection and its Evaluation on the KITTI Road Benchmark. Workshop on Benchmarking Road Terrain and Lane Detection Algorithms for In-Vehicle Application, IEEE Intelligent Vehicles Symposium 2014.
69	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.
70	RES3D-Stereo			81.08 %	81.68 %	78.14 %	84.24 %	12.98 %	15.76 %	0.7 s	1 core @ 2.5 Ghz (C/C++)
P. Shinzato, D. Gomes and D. Wolf: Road estimation with sparse 3D points from stereo data. Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on 2014.
71	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.
72	SPlane + BL			79.63 %	83.90 %	72.59 %	88.17 %	18.34 %	11.83 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
73	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.
74	SPlane			78.69 %	77.16 %	71.96 %	86.80 %	18.63 %	13.20 %	2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
75	ANN			67.70 %	52.50 %	54.19 %	90.17 %	41.98 %	9.83 %	3 s	1 core @ 3.0 Ghz (C/C++)
G. Vitor, D. Lima, A. Victorino and J. Ferreira: A 2D/3D Vision Based Approach Applied to Road Detection in Urban Environments. Intelligent Vehicles Symposium (IV), 2013 IEEE 2013.

The KITTI Vision Benchmark Suite

A project of Karlsruhe Institute of Technologyand Toyota Technological Institute at Chicago

Road/Lane Detection Evaluation 2013

Road Estimation Evaluation

UM_ROAD

UMM_ROAD

UU_ROAD

URBAN_ROAD

Lane Estimation Evaluation

UM_LANE

Behaviour Evaluation

UM_LANE

Related Datasets

Citation

A project of Karlsruhe Institute of Technology
and Toyota Technological Institute at Chicago