Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 17, 2026
Multi-modal Information Learning and Analytics on Cross-Media Data Integration
Article Number 10
Number of page(s) 15
DOI https://doi.org/10.1051/smdo/2026005
Published online 06 April 2026
  1. F. Liu, Z. Lu, X. Lin, Vision-based environmental perception for autonomous driving, Proc. Inst. Mech. Eng. Part D: J. Automob. Eng. 239, 39–69 (2025) [Google Scholar]
  2. S. Lampinen, L. Niu, L. Hulttinen et al., Autonomous robotic rock breaking using a real‐time 3D visual perception system, J. Field Robot. 38, 980–1006 (2021) [Google Scholar]
  3. L. Chen, S. Teng, B. Li et al., Milestones in autonomous driving and intelligent vehicles—Part II: Perception and planning, IEEE Trans. Syst. Man Cybern. Syst. 53, 6401–6415 (2023) [Google Scholar]
  4. Z. Shang, Z. Shen, Topology-based UAV path planning for multi-view stereo 3D reconstruction of complex structures, Complex Intell. Syst. 9, 909–926 (2023) [Google Scholar]
  5. R. Hoseinnezhad, A comprehensive review of deep learning techniques in mobile robot path planning: categorization and analysis, Appl. Sci. 15, 2179 (2025) [Google Scholar]
  6. B. Ma, Q. Liu, Z. Jiang et al., Energy-efficient 3D path planning for complex field scenes using the digital model with landcover and terrain, Isprs Int. J. Geo-Inf. 12, 82 (2023) [Google Scholar]
  7. H. Ma, H. Ma, L. Zhang et al., Extracting urban road footprints from airborne LiDAR point clouds with PointNet++ and two-step post-processing, Remote Sens. 14, 789 (2022) [Google Scholar]
  8. Z. Bai, R. Ji, J. Qi, Deciphering motorists’ perceptions of scenic road visual landscapes: integrating binocular simulation and image segmentation, Land 13, 1381 (2024) [Google Scholar]
  9. V. Arampatzakis, G. Pavlidis, N. Mitianoudis et al., Monocular depth estimation: a thorough review, IEEE Trans. Pattern Anal. Mach. Intell. 46, 2396–2414 (2023) [Google Scholar]
  10. H. Xu, Q. Huang, H. Liao et al., MFFP-Net: building segmentation in remote sensing images via multi-scale feature fusion and foreground perception enhancement, Remote Sens. 17, 1875 (2025) [Google Scholar]
  11. N.U.A. Tahir, Z. Zhang, M. Asim et al., Object detection in autonomous vehicles under adverse weather: a review of traditional and deep learning approaches, Algorithms 17, 103 (2024) [Google Scholar]
  12. Y. Cao, Z. Li, Research on dynamic simulation technology of urban 3D art landscape based on VR‐platform, Math. Probl. Eng. 2022, 3252040 (2022) [Google Scholar]
  13. M. Półrolniczak, L. Kolendowicz, The effect of seasonality and weather conditions on human perception of the urban–rural transitional landscape, Sci. Rep. 13, 15047 (2023) [Google Scholar]
  14. Z. Li, H. Liang, H. Wang et al., MKD-cooper: cooperative 3D object detection for autonomous driving via multi-teacher knowledge distillation, IEEE Trans. Intell. Veh. 9, 1490–1500 (2023) [Google Scholar]
  15. P. Suanpang, P. Jamjuntr, Optimizing autonomous UAV navigation with d* algorithm for sustainable development, Sustainability 16, 7867 (2024) [Google Scholar]
  16. B. Wu, X. Chi, C. Zhao et al., Dynamic path planning for forklift AGV based on smoothing A* and improved DWA hybrid algorithm, Sensors 22, 7079 (2022) [CrossRef] [PubMed] [Google Scholar]
  17. Z. Yaoming, S.U. Yu, X.I.E. Anhuan et al., A newly bio-inspired path planning algorithm for autonomous obstacle avoidance of UAV, Chin. J. Aeronaut. 34, 199–209 (2021) [Google Scholar]
  18. D.H. Lee, J.L. Liu, End-to-end deep learning of lane detection and path prediction for real-time autonomous driving, Signal Image Video Process. 17, 199–205 (2023) [Google Scholar]
  19. C. Liu, T. Sziranyi, Road condition detection and emergency rescue recognition using on-board UAV in the wildness, Remote Sens. 14, 4355 (2022) [Google Scholar]
  20. Y. Gholami, S.H. Taghvaei, S. Norouzian-Maleki et al., Identifying the stimulus of visual perception based on eye-tracking in urban parks: case study of mellat park in Tehran, J. Forest Res. 26, 91–100 (2021) [Google Scholar]
  21. H. Gong, T. Mu, Q. Li et al., Swin-transformer-enabled YOLOv5 with attention mechanism for small object detection on satellite images, Remote Sens. 14, 2861 (2022) [Google Scholar]
  22. Z. Xu, T. Liu, Z. Xia et al., SSG-Net: A multi-branch fault diagnosis method for scroll compressors using swin transformer sliding window, shallow ResNet, and global attention mechanism (GAM), Sensors 24, 6237 (2024) [Google Scholar]
  23. Z. Chen, Z. Wang, X. Gao et al., Channel pruning method for signal modulation recognition deep learning models, IEEE Trans. Cognit. Commun. Netw. 10, 442–453 (2023) [Google Scholar]
  24. K.Y. Feng, X. Fei, M. Gong et al., An automatically layer-wise searching strategy for channel pruning based on task-driven sparsity optimization, IEEE Trans. Circuits Syst. Video Technol. 32, 5790–5802 (2022) [Google Scholar]
  25. Z. Chen, J. Hu, G. Min et al., Adaptive and efficient resource allocation in cloud datacenters using actor-critic deep reinforcement learning, IEEE Trans. Parallel Distrib. Syst. 33, 1911–1923 (2021) [Google Scholar]
  26. Y. Yuan, L. Lei, T.X. Vu et al., Energy minimization in UAV-aided networks: actor-critic learning for constrained scheduling optimization, IEEE Trans. Veh. Technol. 70, 5028–5042 (2021) [Google Scholar]
  27. Y. Jin, X. Song, G. Slabaugh et al., Partial advantage estimator for proximal policy optimization, IEEE Trans. Games 17, 158–166 (2024) [Google Scholar]
  28. K. Jin, L. Wang, X. Wang et al., Offline reinforcement learning combining generalized advantage estimation and modality decomposition interaction, Sci. Rep. 15, 15601 (2025) [Google Scholar]
  29. M. Hafner, M. Katsantoni, T. Köster et al., CLIP and complementary methods, Nat. Rev. Methods Primers 1, 20 (2021) [Google Scholar]
  30. M.C. Bingol, A safe navigation algorithm for differential-drive mobile robots by using fuzzy logic reward function-based deep reinforcement learning, Electronics 14, 1593 (2025) [Google Scholar]
  31. H.H. Goh, Y. Huang, C.S. Lim et al., An assessment of multistage reward function design for deep reinforcement learning-based microgrid energy management, IEEE Trans. Smart Grid 13, 4300–4311 (2022) [CrossRef] [Google Scholar]
  32. L. Chaudhary, B. Singh, Gumbel-SoftMax based graph convolution network approach for community detection, Int. J. Inf. Technol. 15, 3063–3070 (2023) [Google Scholar]
  33. T. Strypsteen, A. Bertrand, End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax, J. Neural Eng. 18, 0460a9 (2021) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.