Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges
Open accessAbstract
Semantic segmentation was seen as a challenging computer vision problem few years ago. Due to recent advancements in deep learning, relatively accurate solutions are now possible for its use in automated driving. In this paper, the semantic segmentation problem is explored from the perspective of automated driving. Most of the current semantic segmentation algorithms are designed for generic images and do not incorporate prior structure and end goal for automated driving. First, the paper begins with a generic taxonomic survey of semantic segmentation algorithms and then discusses how it fits in the context of automated driving. Second, the particular challenges of deploying it into a safety system which needs high level of accuracy and robustness are listed. Third, different alternatives instead of using an independent semantic segmentation module are explored. Finally, an empirical evaluation of various semantic segmentation architectures was performed on CamVid dataset in terms of accuracy and speed. This paper is a preliminary shorter version of a more detailed survey which is work in progress.
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The WoodScape dataset supports correct hypothesis evaluation, and therefore multiple splits are provided (5 in total).
“The dataset supports correct hypothesis evaluation [55], therefore multiple splits are provided (5 in total).”
From WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019) · cites this paper as [55]
The section on large annotated datasets discusses CamVid, Cityscapes and other datasets, but not WoodScape. The benchmarking section describes an evaluation on CamVid; neither it nor the other passages found addresses WoodScape’s support for hypothesis evaluation or its five splits (searched: WoodScape, hypothesis evaluation, dataset splits). (LLM, conf=0.76)
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
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 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392 · see citing works
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392 · see citing works
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433 · see citing works
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_49 · Cited by 7,945 · see citing works
Multi-scale context aggregation by dilated convolutions
Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738 · see citing works
Learning Deconvolution Network for Semantic Segmentation
Noh, H., Hong, S., & Han, B. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.178 · Cited by 4,422 · see citing works
End to End Learning for Self-Driving Cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al. (2016) · arXiv · doi:10.48550/arxiv.1604.07316 · Cited by 3,198 · see citing works
Learning Hierarchical Features for Scene Labeling
Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620 · see citing works
Conditional Random Fields as Recurrent Neural Networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.179 · Cited by 2,386 · cited 1× · see citing works
“Classical Methods Fully Convolutional Networks Structured Models Spatio-Temporal Models Decision Forests [17][18] Patchwise Training [19][20][21] Merged with CRFs [22][23][24] Clockworks Net [25] CRFs [26][27] Pixelwise Training [28][29][30] Using RNNs [31] Using RNNs [32][33][34] Boosting [26][35] Multiscale [36][19]…”
MethodRos, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.352 · Cited by 2,338 · see citing works
DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.312 · Cited by 1,876 · see citing works
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Paszke, A., Chaurasia, A., Kim, S., & Culurciello, E. (2016) · arXiv · doi:10.48550/arxiv.1606.02147 · Cited by 1,826 · see citing works
ALVINN, an autonomous land vehicle in a neural network
Pomerleau, D. A. (2018) · doi:10.1184/r1/6603146 · Cited by 1,403 · see citing works
Semantic object classes in video: A high-definition ground truth database
Brostow, G., Fauqueur, J., & Cipolla, R. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.04.005 · Cited by 1,400 · see citing works
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · arXiv · doi:10.48550/arxiv.1604.01685 · Cited by 1,174 · see citing works
Shotton, J., Winn, J. M., Rother, C., & Criminisi, A. (2006) · Lecture notes in computer science · doi:10.1007/11744023_1 · Cited by 1,154 · see citing works
VirtualWorlds as Proxy for Multi-object Tracking Analysis
Gaidon, A., Wang, Q., Cabon, Y., & Vig, E. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.470 · Cited by 1,079 · see citing works
Segmentation and Recognition Using Structure from Motion Point Clouds
Brostow, G. J., Shotton, J., Fauqueur, J., & Cipolla, R. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_5 · Cited by 1,050 · see citing works
Semantic texton forests for image categorization and segmentation
Shotton, J., Johnson, M., & Cipolla, R. (2008) · 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.2008.4587503 · Cited by 1,027 · see citing works
An Analysis of Deep Neural Network Models for Practical Applications
Canziani, A., Paszke, A., & Culurciello, E. (2016) · arXiv · doi:10.48550/arxiv.1605.07678 · Cited by 969 · see citing works
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
Lin, G., Shen, C., van den Hengel, A., & Reid, I. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.348 · Cited by 862 · see citing works
Scene Text Recognition using Higher Order Language Priors
Mishra, A., Alahari, K., & Jawahar, C. V. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.127 · Cited by 837 · see citing works
ElasticFusion: Dense SLAM Without A Pose Graph
Whelan, T., Leutenegger, S., Salas Moreno, R., Glocker, B., & Davison, A. (2015) · Robotics: Science and Systems XI · doi:10.15607/rss.2015.xi.001 · Cited by 835 · see citing works
Kendall, A., Badrinarayanan, V., & Cipolla, R. (2017) · Procedings of the British Machine Vision Conference 2017 · doi:10.5244/c.31.57 · Cited by 784 · see citing works
DeMoN: Depth and Motion Network for Learning Monocular Stereo
Ummenhofer, B., Zhou, H., Uhrig, J., Mayer, N., Ilg, E., Dosovitskiy, A., & Brox, T. (2016) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2017.596 · Cited by 777 · see citing works
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
Teichmann, M., Weber, M., Zollner, M., Cipolla, R., & Urtasun, R. (2018) · 2018 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2018.8500504 · Cited by 681 · see citing works
Learning Deconvolution Network for Semantic SegmentationPreprint
Noh, H., Hong, S., & Han, B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.04366 · Cited by 613 · see citing works
Associative hierarchical CRFs for object class image segmentation
Ladický, Ľ., Russell, C., Kohli, P., & Torr, P. H. S. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459248 · Cited by 600 · see citing works
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2016) · arXiv · doi:10.48550/arxiv.1610.02357 · Cited by 538 · see citing works
3D U-Net: Learning Dense Volumetric Segmentation from Sparse AnnotationPreprint
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.06650 · Cited by 488 · see citing works
Instance-Sensitive Fully Convolutional Networks
Dai, J., He, K., Li, Y., Ren, S., & Sun, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_32 · Cited by 421 · see citing works
Virtual Worlds as Proxy for Multi-Object Tracking AnalysisPreprint
Gaidon, A., Wang, Q., Cabon, Y., & Vig, E. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06457 · Cited by 302 · see citing works
ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation
Visin, F., Romero, A., Cho, K., Matteucci, M., Ciccone, M., Kastner, K., Bengio, Y., & Courville, A. C. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.60 · Cited by 295 · see citing works
Kendall, A., Badrinarayanan, V., & Cipolla, R. (2015) · arXiv · doi:10.48550/arxiv.1511.02680 · Cited by 292 · see citing works
(2016) · Cited by 271 · see citing works
Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade
Li, X., Liu, Z., Luo, P., Loy, C. C., & Tang, X. (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.2017.684 · Cited by 268 · see citing works
Zhu, H., Meng, F., Cai, J., & Lu, S. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.10.012 · Cited by 233 · see citing works
Joint Semantic Segmentation and 3D Reconstruction from Monocular Video
Kundu, A., Li, Y., Dellaert, F., Li, F., & Rehg, J. M. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10599-4_45 · Cited by 220 · see citing works
Deep Multispectral Semantic Scene Understanding of Forested Environments Using Multimodal Fusion
Valada, A., Oliveira, G. L., Brox, T., & Burgard, W. (2017) · Springer proceedings in advanced robotics · doi:10.1007/978-3-319-50115-4_41 · Cited by 211 · see citing works
Clockwork Convnets for Video Semantic Segmentation
Shelhamer, E., Rakelly, K., Hoffman, J., & Darrell, T. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8_69 · Cited by 204 · see citing works
Combining Appearance and Structure from Motion Features for Road Scene Understanding
Sturgess, P., Alahari, K., Ladický, Ľ., & Torr, P. H. S. (2009) · British Machine Vision Conference (BMVC) · doi:10.5244/c.23.62 · Cited by 201 · see citing works
Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction
Vineet, V., Miksik, O., Lidegaard, M., Niebner, M., Golodetz, S., Prisacariu, V. A., Kahler, O., Murray, D. W., Izadi, S., Peerez, P., et al. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7138983 · Cited by 200 · see citing works
TorontoCity: Seeing the World with a Million Eyes
Wang, S., Bai, M., Mattyus, G., Chu, H., Luo, W., Yang, B., Liang, J., Cheverie, J., Fidler, S., & Urtasun, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.327 · Cited by 186 · see citing works
MultiNet: Real-time Joint Semantic Reasoning for Autonomous DrivingPreprint
Teichmann, M., Weber, M., Zöllner, M., Cipolla, R., & Urtasun, R. (2016) · arXiv (Cornell University) · doi:10.17863/cam.26778 · Cited by 164 · see citing works
DeepDriving: Learning Affordance for Direct Perception in Autonomous DrivingPreprint
Chen, C., Seff, A., Kornhauser, A. L., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.00256 · Cited by 154 · see citing works
Attention to Scale: Scale-aware Semantic Image SegmentationPreprint
Chen, L.-C., Yang, Y., Wang, J., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.03339 · Cited by 133 · see citing works
Convolutional gated recurrent networks for video segmentation
Siam, M., Valipour, S., Jägersand, M., & Ray, N. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8296851 · Cited by 107 · see citing works
Self-citationUnderstanding High-Level Semantics by Modeling Traffic Patterns
Zhang, H., Geiger, A., & Urtasun, R. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.379 · Cited by 87 · see citing works
Efficient piecewise training of deep structured models for semantic segmentationPreprint
Lin, G., Shen, C., van den Hengel, A., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.01013 · Cited by 85 · see citing works
Hierarchically Gated Deep Networks for Semantic Segmentation
Qi, G.-J. (2016) · 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.2016.249 · Cited by 76 · see citing works
Joint Optical Flow and Temporally Consistent Semantic Segmentation
Hur, J., & Roth, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46604-0_12 · Cited by 64 · see citing works
Instance-sensitive Fully Convolutional NetworksPreprint
Dai, J., He, K., Li, Y., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.08678 · Cited by 61 · see citing works
Mikšík, O., Vineet, V., Lidegaard, M., Prasaath, R., Nießner, M., Golodetz, S., Hicks, S. L., Pérez, P., Izadi, S., & Torr, P. H. S. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702222 · Cited by 56 · see citing works
STFCN: Spatio-Temporal FCN for Semantic Video SegmentationPreprint
Fayyaz, M., Saffar, M. H., Sabokrou, M., Fathy, M., Klette, R., & Huang, F. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.05971 · Cited by 45 · see citing works
SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural NetworksPreprint
McCormac, J., Handa, A., Davison, A. J., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.05130 · Cited by 44 · see citing works
Adversarial Deep Structural Networks for Mammographic Mass SegmentationPreprint
Zhu, W., Xiang, X., Tran, T. D., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.05970 · Cited by 43 · see citing works
End-to-end Learning of Driving Models from Large-scale Video DatasetsPreprint
Xu, H., Gao, Y., Yu, F., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.01079 · Cited by 38 · see citing works
Not All Pixels Are Equal: Difficulty-aware Semantic Segmentation via Deep Layer CascadePreprint
Li, X., Liu, Z., Luo, P., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01344 · Cited by 24 · see citing works
Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers
Farabet, C., Couprie, C., Najman, L. A., & LeCun, Y. (2012) · HAL · Cited by 22 · see citing works
TorontoCity: Seeing the World with a Million EyesPreprint
Wang, S., Bai, M., Máttyus, G., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00423 · Cited by 19 · see citing works
Human-Robot Collaboration for Semantic Labeling of the Environment
Bonanni, T. M., Pennisi, A., Bloisi, D. D., Iocchi, L., & Nardi, D. (2013) · CINECA IRIS Institutional Research Information System (University of Basilicata) · Cited by 15 · see citing works
Semantic Video Segmentation by Gated Recurrent Flow PropagationPreprint
Nilsson, D., & Sminchisescu, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.08871 · Cited by 12 · see citing works
Joint Optical Flow and Temporally Consistent Semantic SegmentationPreprint
Hur, J., & Roth, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.07716
Flagged first, then by citations
Research works printed but not linked: 2
- U. Muller, J. Ben, E. Cosatto, B. Flepp, and Y. L. Cun, "Off-road obstacle avoidance through end-to-end learning," in Advances in neural information processing systems, 2005, pp. 739-746.
- D. Grangier, L. Bottou, and R. Collobert, "Deep convolutional networks for scene parsing," in ICML 2009 Deep Learning Workshop, vol. 3. Citeseer, 2009.
Cited by
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Cha, Y., Choi, W., & Büyüköztürk, O. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12263 · Cited by 3,295 · see citing works
Deep reinforcement learning for autonomous driving: A survey
Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Sallab, A. A. A., Yogamani, S., & Perez, P. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3054625 · Cited by 2,342 · see citing works
Self-citationWoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving
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“Semantic segmentation networks for autonomous driving [47] have been successfully trained directly on fisheye images in [12, 45].”
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El Madawi, K., Rashed, H., El Sallab, A., Nasr, O., Kamel, H., & Yogamani, S. (2019) · 2019 IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2019.8917447 · Cited by 116 · see citing works
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Hu, Y., Gao, S., Lunga, D., Li, W., Newsam, S., & Bhaduri, B. (2019) · SIGSPATIAL Special · doi:10.1145/3377000.3377002 · Cited by 58 · see citing works
Let’s Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving
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AuxNet: Auxiliary Tasks Enhanced Semantic Segmentation for Automated Driving
Chennupati, S., Sistu, G., Yogamani, S., & Rawashdeh, S. (2019) · Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications · doi:10.5220/0007684106450652 · Cited by 42 · see citing works
Self-citation
- Authors
- Siam, M., Elkerdawy, S., Jagersand, M., & Yogamani, S.
- Affiliations
- University of Alberta (CA)
- Valeo (Ireland) (IE)
- Venue
- 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1-8
- OpenAlex
- W2734839544
- Cited by
- 178
- References
- 70
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 23 · 2023: 18 · 2022: 14 · 2021: 29 · 2020: 29 · 2019: 43 · 2018: 9 · 2017: 1
- FWCI
- 6.45
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Intelligent Transportation Systems (ITSC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- semantic segmentation, autonomous driving, autonomous vehicles, deep learning architectures, safety-critical systems, robustness, real-time performance, road scene understanding
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