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Robot."},{"issue":"4","key":"10.1016\/j.aei.2026.104657_b79","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2024.3367956","article-title":"Object depth and size estimation using stereo-vision and integration with slam","volume":"8","author":"Hamad","year":"2024","journal-title":"IEEE Sensors Lett."},{"issue":"2","key":"10.1016\/j.aei.2026.104657_b80","doi-asserted-by":"crossref","first-page":"300","DOI":"10.3390\/electronics14020300","article-title":"Depth estimation based on mmwave radar and camera fusion with attention mechanisms and multi-scale features for autonomous driving vehicles","volume":"14","author":"Zhu","year":"2025","journal-title":"Electronics"},{"key":"10.1016\/j.aei.2026.104657_b81","doi-asserted-by":"crossref","unstructured":"Y. Wang, J. Li, C. Hong, R. Li, L. Sun, X. Song, Z. Wang, Z. Cao, G. Lin, Tacodepth: Towards efficient radar-camera depth estimation with one-stage fusion, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2025, pp. 10523\u201310533.","DOI":"10.1109\/CVPR52734.2025.00984"},{"key":"10.1016\/j.aei.2026.104657_b82","series-title":"International Conference on Pattern Recognition","first-page":"463","article-title":"Best of both sides: Integration of absolute and relative depth sensing modalities based on itof and rgb cameras","author":"Fang","year":"2024"},{"issue":"1","key":"10.1016\/j.aei.2026.104657_b83","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s44196-024-00692-5","article-title":"Deep learning algorithm for optimized sensor data fusion in fault diagnosis and tolerance","volume":"17","author":"Elhoseny","year":"2024","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"10.1016\/j.aei.2026.104657_b84","series-title":"2022 ACM\/IEEE 13th International Conference on Cyber-Physical Systems","first-page":"68","article-title":"HydraFusion: Context-aware selective sensor fusion for robust and efficient autonomous vehicle perception","author":"Malawade","year":"2022"},{"key":"10.1016\/j.aei.2026.104657_b85","doi-asserted-by":"crossref","DOI":"10.1016\/j.robot.2024.104680","article-title":"Enhancing lane detection with a lightweight collaborative late fusion model","volume":"175","author":"Jahn","year":"2024","journal-title":"Robot. Auton. Syst."},{"issue":"3","key":"10.1016\/j.aei.2026.104657_b86","first-page":"2509","article-title":"Bridging 2d and 3d object detection: Advances in occlusion handling through depth estimation","volume":"143","author":"Ouardirhi","year":"2025","journal-title":"Comput. Model. Eng. Sci."},{"key":"10.1016\/j.aei.2026.104657_b87","series-title":"2025 IEEE International Conference on Robotics and Automation","first-page":"6299","article-title":"Bridging spectral-wise and multi-spectral depth estimation via geometry-guided contrastive learning","author":"Shin","year":"2025"},{"key":"10.1016\/j.aei.2026.104657_b88","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127859","article-title":"Region-focused CNN with dynamic adaptive graph attention network for stereogram evoked EEG recognition","author":"Yan","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104657_b89","series-title":"Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, the Netherlands, October 11\u201314, 2016, Proceedings, Part I 14","first-page":"749","article-title":"Learning to track at 100 fps with deep regression networks","author":"Held","year":"2016"},{"key":"10.1016\/j.aei.2026.104657_b90","doi-asserted-by":"crossref","unstructured":"Z. Qin, J. Wang, Y. Lu, Triangulation learning network: from monocular to stereo 3d object detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 7615\u20137623.","DOI":"10.1109\/CVPR.2019.00780"},{"issue":"1","key":"10.1016\/j.aei.2026.104657_b91","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TIV.2016.2578706","article-title":"A survey of motion planning and control techniques for self-driving urban vehicles","volume":"1","author":"Paden","year":"2016","journal-title":"IEEE Trans. Intell. Veh."},{"key":"10.1016\/j.aei.2026.104657_b92","doi-asserted-by":"crossref","unstructured":"K. Takumi, K. Watanabe, Q. Ha, A. Tejero-De-Pablos, Y. Ushiku, T. Harada, Multispectral object detection for autonomous vehicles, in: Proceedings of the on Thematic Workshops of ACM Multimedia 2017, 2017, pp. 35\u201343.","DOI":"10.1145\/3126686.3126727"},{"key":"10.1016\/j.aei.2026.104657_b93","doi-asserted-by":"crossref","unstructured":"J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, et al., Speed\/accuracy trade-offs for modern convolutional object detectors, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 7310\u20137311.","DOI":"10.1109\/CVPR.2017.351"},{"key":"10.1016\/j.aei.2026.104657_b94","series-title":"IFIP International Internet of Things Conference","first-page":"36","article-title":"Computer vision based 3D model floor construction for smart parking system","author":"Patra","year":"2023"},{"key":"10.1016\/j.aei.2026.104657_b95","doi-asserted-by":"crossref","DOI":"10.1016\/j.nhres.2025.02.001","article-title":"Lightweight CNN model for automatic detection and depth estimation of subsurface voids using GPR B-scan data","author":"Mojahid","year":"2025","journal-title":"Nat. Hazards Res."},{"key":"10.1016\/j.aei.2026.104657_b96","series-title":"2018 21st International Conference on Intelligent Transportation Systems","first-page":"3873","article-title":"Uncertainty estimation for deep neural object detectors in safety-critical applications","author":"Le","year":"2018"},{"key":"10.1016\/j.aei.2026.104657_b97","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.aei.2026.104657_b98","doi-asserted-by":"crossref","unstructured":"X. Bai, Z. Hu, X. Zhu, Q. Huang, Y. Chen, H. Fu, C.-L. Tai, Transfusion: Robust lidar-camera fusion for 3d object detection with transformers, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 1090\u20131099.","DOI":"10.1109\/CVPR52688.2022.00116"},{"key":"10.1016\/j.aei.2026.104657_b99","series-title":"International Conference on Intelligent Computing","first-page":"133","article-title":"Lidar-camera-based deep dense fusion for robust 3D object detection","author":"Wen","year":"2020"},{"issue":"6","key":"10.1016\/j.aei.2026.104657_b100","doi-asserted-by":"crossref","first-page":"2972","DOI":"10.3390\/s23062972","article-title":"Empirical analysis of autonomous vehicle\u2019s lidar detection performance degradation for actual road driving in rain and fog","volume":"23","author":"Kim","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.aei.2026.104657_b101","series-title":"Fusing bird view lidar point cloud and front view camera image for deep object detection","author":"Wang","year":"2017"},{"key":"10.1016\/j.aei.2026.104657_b102","series-title":"2018 IEEE Intelligent Vehicles Symposium","first-page":"1","article-title":"Fusing bird\u2019s eye view lidar point cloud and front view camera image for 3d object detection","author":"Wang","year":"2018"},{"issue":"2","key":"10.1016\/j.aei.2026.104657_b103","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1109\/JSEN.2020.3020626","article-title":"Deep 3D object detection networks using LiDAR data: A review","volume":"21","author":"Wu","year":"2020","journal-title":"IEEE Sensors J."},{"key":"10.1016\/j.aei.2026.104657_b104","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.patrec.2017.09.038","article-title":"Multimodal vehicle detection: fusing 3D-LIDAR and color camera data","volume":"115","author":"Asvadi","year":"2018","journal-title":"Pattern Recognit. Lett."},{"issue":"9","key":"10.1016\/j.aei.2026.104657_b105","doi-asserted-by":"crossref","first-page":"16249","DOI":"10.1109\/TITS.2022.3149370","article-title":"Towards compact autonomous driving perception with balanced learning and multi-sensor fusion","volume":"23","author":"Natan","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aei.2026.104657_b106","series-title":"2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE)","first-page":"1146","article-title":"Autonomous multi-sensor fusion techniques for environmental perception in self-driving vehicles","author":"Sumalatha","year":"2024"},{"issue":"3","key":"10.1016\/j.aei.2026.104657_b107","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3650039","article-title":"Learning cross-modality interaction for robust depth perception of autonomous driving","volume":"15","author":"Liang","year":"2024","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.aei.2026.104657_b108","series-title":"2015 IEEE International Conference on Robotics and Automation","first-page":"2814","article-title":"Fast LIDAR localization using multiresolution Gaussian mixture maps","author":"Wolcott","year":"2015"},{"key":"10.1016\/j.aei.2026.104657_b109","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.robot.2018.11.002","article-title":"LIDAR\u2013camera fusion for road detection using fully convolutional neural networks","volume":"111","author":"Caltagirone","year":"2019","journal-title":"Robot. Auton. Syst."},{"key":"10.1016\/j.aei.2026.104657_b110","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2024.105335","article-title":"A lightweight depth completion network with spatial efficient fusion","volume":"153","author":"Fu","year":"2025","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.aei.2026.104657_b111","doi-asserted-by":"crossref","unstructured":"Y. Wang, W.-L. Chao, D. Garg, B. Hariharan, M. Campbell, K.Q. Weinberger, Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 8445\u20138453.","DOI":"10.1109\/CVPR.2019.00864"},{"issue":"3","key":"10.1016\/j.aei.2026.104657_b112","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s00138-011-0404-2","article-title":"Recent progress in road and lane detection: a survey","volume":"25","author":"Bar Hillel","year":"2014","journal-title":"Mach. Vis. Appl."},{"issue":"10","key":"10.1016\/j.aei.2026.104657_b113","doi-asserted-by":"crossref","first-page":"2967","DOI":"10.1109\/TIP.2011.2142006","article-title":"A multilevel mixture-of-experts framework for pedestrian classification","volume":"20","author":"Enzweiler","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.aei.2026.104657_b114","series-title":"Statistical multisource-multitarget information fusion","author":"Mahler","year":"2007"},{"key":"10.1016\/j.aei.2026.104657_b115","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108550","article-title":"An in-depth evaluation of deep learning-enabled adaptive approaches for detecting obstacles using sensor-fused data in autonomous vehicles","volume":"133","author":"Thakur","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"7540","key":"10.1016\/j.aei.2026.104657_b116","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.aei.2026.104657_b117","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","article-title":"Deep learning for generic object detection: A survey","volume":"128","author":"Liu","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.aei.2026.104657_b118","unstructured":"C.R. Qi, H. Su, K. Mo, L.J. Guibas, Pointnet: Deep learning on point sets for 3d classification and segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 652\u2013660."},{"issue":"3","key":"10.1016\/j.aei.2026.104657_b119","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TITS.2020.2972974","article-title":"Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges","volume":"22","author":"Feng","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aei.2026.104657_b120","doi-asserted-by":"crossref","unstructured":"Y. Xiang, A. Alahi, S. Savarese, Learning to track: Online multi-object tracking by decision making, in: Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 4705\u20134713.","DOI":"10.1109\/ICCV.2015.534"},{"key":"10.1016\/j.aei.2026.104657_b121","series-title":"Conference on Robot Learning","first-page":"1","article-title":"CARLA: An open urban driving simulator","author":"Dosovitskiy","year":"2017"},{"key":"10.1016\/j.aei.2026.104657_b122","series-title":"2012 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3354","article-title":"Are we ready for autonomous driving? the kitti vision benchmark suite","author":"Geiger","year":"2012"},{"issue":"2","key":"10.1016\/j.aei.2026.104657_b123","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3434398","article-title":"Object detection using deep learning methods in traffic scenarios","volume":"54","author":"Boukerche","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.aei.2026.104657_b124","series-title":"Learning multimodal fixed-point weights using gradient descent","author":"Enderich","year":"2019"},{"key":"10.1016\/j.aei.2026.104657_b125","series-title":"2023 IEEE International Conference on Robotics and Automation","first-page":"2774","article-title":"Bevfusion: Multi-task multi-sensor fusion with unified bird\u2019s-eye view representation","author":"Liu","year":"2023"},{"issue":"11","key":"10.1016\/j.aei.2026.104657_b126","doi-asserted-by":"crossref","first-page":"3980","DOI":"10.1109\/TCYB.2016.2593940","article-title":"On-board object detection: Multicue, multimodal, and multiview random forest of local experts","volume":"47","author":"Gonz\u00e1lez","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.aei.2026.104657_b127","series-title":"2020 IEEE 23rd International Conference on Information Fusion","first-page":"1","article-title":"Early vs late fusion in multimodal convolutional neural networks","author":"Gadzicki","year":"2020"},{"key":"10.1016\/j.aei.2026.104657_b128","series-title":"2018 21st International Conference on Intelligent Transportation Systems","first-page":"3266","article-title":"Towards safe autonomous driving: Capture uncertainty in the deep neural network for lidar 3d vehicle detection","author":"Feng","year":"2018"}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626003496?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626003496?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T17:58:25Z","timestamp":1775757505000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626003496"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":128,"alternative-id":["S1474034626003496"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104657","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Advances in multi-sensor fusion for depth estimation in autonomous vehicles: A comprehensive survey","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104657","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. 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