The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
A Novel Cognitively Inspired Deep Learning Approach to Detect Drivable Areas for Self-driving Cars
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Route planning in ICV systems aims to guide the efficient vehicular movement between starting and ending points, and its complexity rises in multi-destination scenarios [84].”
From Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy (2025) · cites this paper as [84]
Citations
Cites
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 43,398
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1993.341010 · Cited by 14,265
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
Yu, F., Koltun, V., & Funkhouser, T. A. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.75 · Cited by 1,942
1 year, 1000 km: The Oxford RobotCar dataset
Maddern, W., Pascoe, G., Linegar, C., & Newman, P. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364916679498 · Cited by 1,632 · cited 1×
“Figure 1 displays three examples of driving scenes and the corresponding driv-able areas labels from the ORD dataset [6].”
Planning-oriented Autonomous Driving
Hu, Y., Yang, J., Chen, L., Li, K., Sima, C., Zhu, X., Chai, S., Du, S., Lin, T., Wang, W., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01712 · Cited by 931
Illingworth, J., & Kittler, J. (1987) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1987.4767964 · Cited by 693
A new performance measure and evaluation benchmark for road detection algorithms
Fritsch, J., Kuhnl, T., & Geiger, A. (2013) · 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) · doi:10.1109/itsc.2013.6728473 · Cited by 690
Mogelmose, A., Trivedi, M. M., & Moeslund, T. B. (2012) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2012.2209421 · Cited by 684
LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images
Li, H., Xu, T., Wu, X.-J., Lu, J., & Kittler, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3268209 · Cited by 493
Real-Time Correlation-Based Stereo Vision with Reduced Border Errors
Hirschmüller, H., Innocent, P. R., & Garibaldi, J. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014554110407 · Cited by 454
Frequency-tuned salient region detection
Achanta, R., Hemami, S. S., Estrada, F., & Susstrunk, S. E. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvprw.2009.5206596 · Cited by 378
Zhu, Y., & Newsam, S. (2017) · 2017 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2017.8296389 · Cited by 314
A review of lane detection methods based on deep learning
Tang, J., Li, S., & Liu, P. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107623 · Cited by 242
A novel lane detection based on geometrical model and Gabor filter
Zhou, S., Jiang, Y., Xi, J., Gong, J., Xiong, G., & Chen, H. (2010) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2010.5548087 · Cited by 236
Fast Vanishing-Point Detection in Unstructured Environments
Moghadam, P., Starzyk, J. A., & Wijesoma, W. S. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2162422 · Cited by 151
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks
Räuker, T., Ho, A., Casper, S., & Hadfield-Menell, D. (2023) · 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml54575.2023.00039 · Cited by 122
Dynamic Fusion Module Evolves Drivable Area and Road Anomaly Detection: A Benchmark and Algorithms
Wang, H., Fan, R., Sun, Y., & Liu, M. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3064089 · Cited by 108
Where Does the Driver Look? Top-Down-Based Saliency Detection in a Traffic Driving Environment
Deng, T., Yang, K.-F., Li, Y., & Yan, H. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2535402 · Cited by 106
Semantic Segmentation of Underwater Images Based on Improved Deeplab
Liu, F., & Fang, M. (2020) · Journal of Marine Science and Engineering · doi:10.3390/jmse8030188 · Cited by 90
Are Deep Neural Networks Adequate Behavioral Models of Human Visual Perception?
Wichmann, F. A., & Geirhos, R. (2023) · Annual Review of Vision Science · doi:10.1146/annurev-vision-120522-031739 · Cited by 87
Brain Computations and Connectivity
Rolls, E. T. (2023) · doi:10.1093/oso/9780198887911.001.0001 · Cited by 76
Neural Map Prior for Autonomous Driving
Xiong, X., Liu, Y., Yuan, T., Wang, Y., Wang, Y., & Zhao, H. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01682 · Cited by 70
Li, Y., Ding, W., Zhang, X., & Ju, Z. (2016) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2016.08.003 · Cited by 46
Robust Visual Saliency Optimization Based on Bidirectional Markov Chains
Jiang, F., Kong, B., Li, J., Dashtipour, K. K., & Gogate, M. (2020) · Cognitive Computation · doi:10.1007/s12559-020-09724-6 · Cited by 35
Self-citationRoad direction detection based on vanishing-point tracking
Moghadam, P., & Dong, J. F. (2012) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2012.6386089 · Cited by 34
A Novel Shadow-Free Feature Extractor for Real-Time Road Detection
Ying, Z., Li, G., Zang, X., Wang, R., & Wang, W. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2967294 · Cited by 23
CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation
Wang, X., Liu, J., Mei, T., & Luo, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3263387 · Cited by 21
A novel visual attention method for target detection from SAR images
Gao, F., Liu, A., Liu, K., Yang, E., & Hussain, A. (2019) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2019.03.021 · Cited by 20
Drivable Area Detection Using Deep Learning Models for Autonomous Driving
Qiao, D., & Zulkernine, F. H. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671392 · Cited by 20
Rawashdeh, N. A., Bos, J. P., & Abu-Alrub, N. J. (2022) · Optical Engineering · doi:10.1117/1.oe.62.3.031202 · Cited by 19
An Illumination-Robust Approach for Feature-Based Road Detection
Ying, Z., Li, G., & Tan, G. (2015) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2015.46 · Cited by 12
A Cognitive Framework for Delegation Between Error-Prone AI and Human Agents
Fuchs, A., Passarella, A., & Conti, M. (2022) · IEEE International Conference on Smart Computing · doi:10.1109/smartcomp55677.2022.00074 · Cited by 11
Zhong, C., Li, B., & Wu, T. (2022) · Remote Sensing · doi:10.3390/rs15010027 · Cited by 11
A Texture-Based Local Soft Voting Method for Vanishing Point Detection from a Single Road Image
Trung Hieu, B. U. I., Nobuyama, E., & Saitoh, T. (2013) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.e96.d.690 · Cited by 10
CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in Robotics
Ayub, A., & Wagner, A. R. (2023) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2023.3299755 · Cited by 8
Detection of the drivable area on high-speed road via YOLACT
Wang, G., Zhang, B., Wang, H., Xu, L. H., Li, Y., & Liu, Z. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-021-02117-8 · Cited by 7
Simple road detection based on vanishing point
Chen, Z., & He, Z. (2014) · Journal of Electronic Imaging · doi:10.1117/1.jei.23.3.033015 · Cited by 6
Acun, O., Küçükmanіşa, A., Genç, Y., & Urhan, O. (2023) · Journal of Real-Time Image Processing · doi:10.1007/s11554-023-01279-7 · Cited by 5
Mochizuki-Freeman, J., Maini, S. S., & Tiganj, Z. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191578 · Cited by 3
Research works printed but not linked: 4
- Jiqing C, Depeng W, Teng L, Tian L, Huabin W. All-weather road drivable area segmentation method based on cyclegan. Vis Comput. 2022;39:1–17.
- Fan C, Jingbo X, Di S. Lane detection based on machine learning algorithm. TELKOMNIKA Indones J Electr Eng. 2014;12(2):1403–9.
- Li Y, Ren J, Yan Y, Liu Q, Ma P, Petrovski A, Sun H. Cbanet: An end-to-end cross band 2-d attention network for hyperspectral change detection in remote sensing. IEEE Trans Geosci Remote Sens. 2023;61:1–11.
- Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861 [Preprint]. 2017. Available from: http://arxiv.org/abs/1704.04861. · arXiv
Cited by
Vehicle-to-Everything Communication in Intelligent Connected Vehicles: A Survey and Taxonomy
Zhang, X., Li, J., Zhou, J., Zhang, S., Wang, J., Yuan, Y., Liu, J., & Li, J. (2025) · Automotive Innovation · doi:10.1007/s42154-024-00310-2 · Cited by 103
Metadata
- Authors
- Jiang, F., Wang, Z., & Yue, G.
- Affiliations
- University of Science and Technology of China (CN)
- Anhui Sanlian University (CN)
- Corresponding author
- Jiang, Fengling
- Yue, Guoqing
- ORCID
- Jiang, Fengling
- Venue
- Cognitive Computation
- Published
- 2023-10-31
- Volume
- 16
- Issue
- 2
- Pages
- 517-533
- ISSN
- 1866-9956 (ISSN-L)
- 1866-9964
- OpenAlex
- W4388048626
- Cited by
- 9
- References
- 43
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 2
- FWCI
- 0.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
- Locations
- Cognitive Computation (publishedVersion)
- Funders
- the Natural Science Foundation of the Education Bureau of Anhui Province (2022AH052130)
- the project of development of an autonomous vehicle management system (HXXM2022168)
- the Key Research Project of Natural Science in Higher Education of Anhui Province (2022AH051994)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- drivable area detection, autonomous vehicles, salient region detection, driver attention, attention mechanism, road boundary detection, vanishing point detection, DeepLabV3+, drivable area segmentation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
