Transformer-based sensor fusion for autonomous vehicles: A comprehensive review
Open accessAbstract
Sensor fusion is vital for many critical applications, including robotics, autonomous driving, aerospace, and beyond. Integrating data streams from different sensors enables us to overcome the intrinsic limitations of each sensor, providing more reliable measurements and reducing uncertainty. Moreover, deep learning-based sensor fusion unlocked the possibility of multimodal learning, which utilizes different sensor modalities to boost object detection. Yet, adverse weather conditions remain a significant challenge to the reliability of sensor fusion. However, introducing the Transformer deep learning model in sensor fusion presents a promising avenue for advancing its sensing capabilities, potentially overcoming that challenge. Transformer models proved powerful in modeling vision, language, and numerous other domains. However, these models suffer from high latency and heavy computation requirements. This paper aims to provide: 1) an extensive overview of sensor fusion and transformer models; 2) an in-depth survey of the state-of-the-art (SoTA) methods for Transformer-based sensor fusion, focusing on camera-LiDAR and camera-radar methods; and 3) a quantitative analysis of the SoTA methods, uncovering research gaps and stimulating future work.
Integrity signals
Registry
Cites
Statistics
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?
Relying on traditional single-modal approaches such as RGB images can encounter limitations in complex scenarios such as occlusion, lighting variations, and multi-object ambiguities.
“Relying on traditional single-modal approaches (e.g., solely using RGB images) can encounter various limitations in complex scenarios, such as occlusion, lighting variations, and multi-object ambiguities [32,33,34].”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [34]
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667 · see citing works
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534 · see citing works
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · 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
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152 · see citing works
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018 · see citing works
Multi-view 3D Object Detection Network for Autonomous Driving
Chen, X., Ma, H., Wan, J., Li, B., & Xia, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.691 · Cited by 3,450 · see citing works
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00252 · Cited by 3,437 · see citing works
Multisensor data fusion: A review of the state-of-the-art
Khaleghi, B., Khamis, A. M., Karray, F. O., & Razavi, S. (2011) · Information Fusion · doi:10.1016/j.inffus.2011.08.001 · Cited by 2,097 · see citing works
Deformable DETR: Deformable Transformers for End-to-End Object Detection
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., & Dai, J. (2020) · arXiv · doi:10.48550/arxiv.2010.04159 · Cited by 1,837 · see citing works
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Gu, A., & Dao, T. (2023) · arXiv · doi:10.48550/arxiv.2312.00752 · Cited by 1,579 · see citing works
Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2972974 · Cited by 1,425 · see citing works
iTransformer: Inverted Transformers Are Effective for Time Series ForecastingPreprint
Liu, Y., Hu, T., Zhang, H., Wu, H., Wang, S., Ma, L., & Long, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.06625 · Cited by 1,287 · see citing works
Lift, Splat, Shoot: Encoding Images from Arbitrary Camera Rigs by Implicitly Unprojecting to 3D
Philion, J., & Fidler, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58568-6_12 · Cited by 1,271 · see citing works
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space ModelPreprint
Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., & Wang, X. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.09417 · Cited by 1,260 · see citing works
Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review
Yeong, D. J., Velasco-Hernandez, G., Barry, J., & Walsh, J. L. (2021) · Sensors · doi:10.3390/s21062140 · Cited by 1,096 · see citing works
TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers
Bai, X., Hu, Z., Zhu, X., Huang, Q., Chen, Y., Fu, H., & Tai, C.-L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00116 · Cited by 990 · see citing works
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
Xu, D., Anguelov, D., & Jain, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00033 · Cited by 856 · see citing works
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
Katharopoulos, A., Vyas, A., Pappas, N., & Fleuret, F. (2020) · arXiv · doi:10.48550/arxiv.2006.16236 · Cited by 827 · see citing works
Multi-Task Multi-Sensor Fusion for 3D Object Detection
Liang, M., Yang, B., Chen, Y., Hu, R., & Urtasun, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00752 · Cited by 723 · see citing works
The ApolloScape Open Dataset for Autonomous Driving and Its Application
Huang, X., Wang, P., Cheng, X., Zhou, D., Geng, Q., & Yang, R. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2926463 · Cited by 615 · see citing works
CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection
Pang, S., Morris, D., & Radha, H. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341791 · Cited by 581 · see citing works
Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review
Fayyad, J., Jaradat, M. A., Gruyer, D., & Najjaran, H. (2020) · Sensors · doi:10.3390/s20154220 · Cited by 529 · see citing works
TransFuser: Imitation With Transformer-Based Sensor Fusion for Autonomous Driving
Chitta, K., Prakash, A., Jaeger, B., Yu, Z., Renz, K., & Geiger, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3200245 · Cited by 528 · see citing works
Multi-Sensor Fusion in Automated Driving: A Survey
Wang, Z., Wu, Y., & Niu, Q. (2020) · IEEE Access · doi:10.1109/access.2019.2962554 · Cited by 526 · see citing works
IoU Loss for 2D/3D Object Detection
Zhou, D., Fang, J., Song, X., Guan, C., Yin, J., Dai, Y., & Yang, R. (2019) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2019.00019 · Cited by 483 · see citing works
Privacy‐preserving federated learning based on multi‐key homomorphic encryption
Ma, J., Naas, S., Sigg, S., & Lyu, X. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22818 · Cited by 458 · see citing works
Cooper: Cooperative Perception for Connected Autonomous Vehicles Based on 3D Point Clouds
Chen, Q., Tang, S., Yang, Q., & Fu, S. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00058 · Cited by 452 · see citing works
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
Shen, S., Dong, Z., Ye, J., Ma, L., Yao, Z., Gholami, A., Mahoney, M. W., & Keutzer, K. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6409 · Cited by 437 · see citing works
A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection
Nobis, F., Geisslinger, M., Weber, M., Betz, J., & Lienkamp, M. (2019) · Sensor Data Fusion: Trends, Solutions, Applications (SDF) · doi:10.1109/sdf.2019.8916629 · Cited by 360 · see citing works
Arthurs, P., Gillam, L., Krause, P., Wang, N., Halder, K., & Mouzakitis, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3084396 · Cited by 339 · see citing works
Advances in Multi-Sensor Data Fusion: Algorithms and Applications
Jiang, D., Zhuang, D., Huang, Y., & Fu, J. (2009) · Sensors · doi:10.3390/s91007771 · Cited by 335 · see citing works
BEVFusion: A Simple and Robust LiDAR-Camera Fusion FrameworkPreprint
Liang, T., Xie, H., Yu, K., Xia, Z., Lin, Z., Wang, Y., Tang, T., Wang, B., & Tang, Z. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.13790 · Cited by 310 · see citing works
xLSTM: Extended Long Short-Term Memory
Beck, M., Pöppel, K., Spanring, M., Auer, A., Prudnikova, O., Kopp, M., Klambauer, G., Brandstetter, J., & Hochreiter, S. (2024) · Advances in Neural Information Processing Systems 37 · doi:10.52202/079017-3417 · Cited by 286 · see citing works
Over-the-Air Computation Systems: Optimization, Analysis and Scaling Laws
Liu, W., Zang, X., Li, Y., & Vucetic, B. S. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.2993703 · Cited by 254 · see citing works
Chellapandi, V. P., Yuan, L., Brinton, C. G., Żak, S. H., & Wang, Z. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3332675 · Cited by 249 · see citing works
Fusionpainting: Multimodal fusion with adaptive attention for 3d object detection
Xu, S., Zhou, D., Fang, J., Yin, J., Bin, Z., & Zhang, L. (2021) · 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc48978.2021.9564951 · Cited by 237 · see citing works
Huang, Z., Lv, C., Xing, Y., & Wu, J. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3003121 · Cited by 194 · see citing works
Cross Modal Transformer: Towards Fast and Robust 3D Object Detection
Yan, J., Liu, Y., Sun, J., Jia, F., Li, S., Wang, T., & Zhang, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01675 · Cited by 182 · see citing works
Multi-modal Sensor Fusion for Auto Driving Perception: A SurveyPreprint
Huang, K., Shi, B., Li, X., Li, X., Huang, S., & Li, Y. (2022) · arXiv (Cornell University) · doi:10.4230/lipics.ecrts.2025.6 · Cited by 181 · see citing works
Multi-Sensor Fusion Technology for 3D Object Detection in Autonomous Driving: A Review
Wang, X., Li, K., & Chehri, A. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3317372 · Cited by 168 · see citing works
AMZ Driverless: The full autonomous racing system
Kabzan, J., Valls, M. D. L. I., Reijgwart, V., Hendrikx, H. F. C., Ehmke, C., Prajapat, M., Bühler, A., Gosala, N., Gupta, M., Sivanesan, R., et al. (2020) · Journal of Field Robotics · doi:10.1002/rob.21977 · Cited by 162 · see citing works
Butt, F. A., Chattha, J. N., Ahmad, J., Zia, M. U., Rizwan, M., & Naqvi, I. H. (2022) · IEEE Access · doi:10.1109/access.2022.3145972 · Cited by 140 · see citing works
CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer
Kim, Y., Kim, S., Choi, J. W., & Kum, D. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25198 · Cited by 130 · see citing works
State space model for new generation network alternative to transformers: a survey
Wang, X., Wang, S., Ding, Y., Li, Y.-H., Wu, W.-T., Rong, Y., Kong, W.-Z., Huang, J., Li, S.-H., Yang, H.-X., et al. (2024) · Visual Intelligence · doi:10.1007/s44267-026-00133-1 · Cited by 118 · see citing works
Rashed, H., Ramzy, M., Vaquero, V., Sallab, A. E., Sistu, G., & Yogamani, S. K. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00293 · Cited by 116 · see citing works
FL-FD: Federated learning-based fall detection with multimodal data fusion
Qi, P., Chiaro, D., & Piccialli, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101890 · Cited by 116 · see citing works
Machine learning and blockchain technologies for cybersecurity in connected vehicles
Ahmad, J., Zia, M. U., Naqvi, I. H., Chattha, J. N., Butt, F. A., Huang, T., & Xiang, W. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1515 · Cited by 94 · see citing works
Transformers in Speech Processing: A SurveyPreprint
Latif, S., Zaidi, A., Cuayáhuitl, H., Shamshad, F., Shoukat, M., Usama, M., & Qadir, J. (2023) · arXiv.org · doi:10.48550/arxiv.2303.11607 · Cited by 90 · see citing works
Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges
Srivastav, A., & Mandal, S. (2023) · IEEE Access · doi:10.1109/access.2023.3312382 · Cited by 83 · see citing works
Gated2Depth: Real-Time Dense Lidar From Gated Images
Gruber, T., Julca-Aguilar, F., Bijelic, M., & Heide, F. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00159 · Cited by 79 · see citing works
Abbasi, A. R. (2022) · Electrical Engineering · doi:10.1007/s00202-022-01590-9 · Cited by 73 · see citing works
MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion
Wu, Z., Chen, G., Gan, Y., Wang, L., & Pu, J. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161329 · Cited by 68 · see citing works
4D-Net for Learned Multi-Modal Alignment
Piergiovanni, A., Casser, V., Ryoo, M. S., & Angelova, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01515 · Cited by 64 · see citing works
Optimal Sensor Data Fusion Architecture for Object Detection in Adverse Weather Conditions
Pfeuffer, A., & Dietmayer, K. (2018) · International Conference on Information Fusion (FUSION) · doi:10.23919/icif.2018.8455757 · Cited by 54 · see citing works
Montañez-Sogamoso, Ó. J., Barón, M. J. S., & Fernández, E. A. (2023) · Remote Sensing · doi:10.3390/rs15133396 · Cited by 54 · see citing works
Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion
Pfeuffer, A., & Dietmayer, K. (2019) · 2019 22th International Conference on Information Fusion (FUSION) · doi:10.23919/fusion43075.2019.9011192 · Cited by 51 · see citing works
Xu, C., & Sankar, R. T. (2024) · Algorithms · doi:10.3390/a17110526 · Cited by 50 · see citing works
Domain Adaptation Based Object Detection for Autonomous Driving in Foggy and Rainy Weather
Li, J., Xu, R., Liu, X., Ma, J., Li, B., Zou, Q., Ma, J., & Yu, H. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3419689 · Cited by 43 · see citing works
Transformer-Based Sensor Fusion for Autonomous Driving: A Survey
Singh, A. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00355 · Cited by 42 · see citing works
Deep Transfer Learning With Self-Attention for Industry Sensor Fusion Tasks
Zhang, Z., Farnsworth, M., Song, B., Tiwari, D., & Tiwari, A. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3186505 · Cited by 41 · see citing works
LIFT: Learning 4D LiDAR Image Fusion Transformer for 3D Object Detection
Zeng, Y., Zhang, D., Wang, C., Miao, Z., Liu, T., Zhan, X., Hao, D., & Ma, C. (2022) · 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/cvpr52688.2022.01666 · Cited by 38 · see citing works
Mauri, A., Khemmar, R., Decoux, B., Haddad, M., & Boutteau, R. (2022) · Journal of Real-Time Image Processing · doi:10.1007/s11554-022-01202-6 · Cited by 37 · see citing works
John, V., & Mita, S. (2021) · Electronics · doi:10.3390/electronics10040424 · Cited by 34 · see citing works
DPFT: Dual Perspective Fusion Transformer for Camera-Radar-Based Object Detection
Fent, F., Palffy, A., & Caesar, H. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3507538 · Cited by 34 · see citing works
End-to-End Multimodal Sensor Dataset Collection Framework for Autonomous Vehicles
Gu, J., Lind, A., Chhetri, T. R., Bellone, M., & Sell, R. (2023) · Sensors · doi:10.3390/s23156783 · Cited by 30 · see citing works
TransCAR: Transformer-Based Camera-and-Radar Fusion for 3D Object Detection
Pang, S., Morris, D., & Radha, H. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341793 · Cited by 30 · see citing works
The Effect of Rainfall and Illumination on Automotive Sensors Detection Performance
Li, H., Bamminger, N., Magosi, Z. F., Feichtinger, C., Zhao, Y., Mihalj, T., Orucevic, F., & Eichberger, A. (2023) · Sustainability · doi:10.3390/su15097260 · Cited by 27 · see citing works
Pay "Attention" to Adverse Weather: Weather-aware Attention-based Object Detection
Chaturvedi, S. S., Zhang, L., & Yuan, X. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956149 · Cited by 25 · see citing works
Multi-Modal Contrastive Learning for LiDAR Point Cloud Rail-Obstacle Detection in Complex Weather
Wen, L., Peng, Y., Lin, M., Gan, N., & Tan, R. (2024) · Electronics · doi:10.3390/electronics13010220 · Cited by 25 · see citing works
Transformer-Based Optimized Multimodal Fusion for 3D Object Detection in Autonomous Driving
Alaba, S. Y., & Ball, J. E. (2024) · IEEE Access · doi:10.1109/access.2024.3385439 · Cited by 20 · see citing works
Alaba, S. Y., Gürbüz, A. C., & Ball, J. E. (2022) · doi:10.36227/techrxiv.20443107.v2 · Cited by 19 · see citing works
RCDPT: Radar-Camera Fusion Dense Prediction Transformer
Lo, C.-C., & Vandewalle, P. (2023) · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49357.2023.10096129 · Cited by 18 · see citing works
CR-DINO: A Novel Camera-Radar Fusion 2-D Object Detection Model Based on Transformer
Jin, Y., Zhu, X., Yue, Y., Lim, E. G., & Wang, W. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3357775 · Cited by 18 · see citing works
Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm
Shahi, K. R., Ghamisi, P., Rasti, B., Jackisch, R., Scheunders, P., & Gloaguen, R. (2020) · Remote Sensing · doi:10.3390/rs12234007 · Cited by 17 · see citing works
Multi-modality Cascaded Fusion Technology for Autonomous Driving
Kuang, H., Liu, X., Zhang, J., & Fang, Z. (2020) · International Conference on Robotics and Automation Sciences (ICRAS) · doi:10.1109/icras49812.2020.9135065 · Cited by 15 · see citing works
A Sensor Fusion Method Using Transfer Learning Models for Equipment Condition Monitoring
Çinar, E. M. (2022) · Sensors · doi:10.3390/s22186791 · Cited by 15 · see citing works
Kowalczyk, P., Komorkiewicz, M., Skruch, P., & Szelest, M. (2022) · IEEE Access · doi:10.1109/access.2022.3145192 · Cited by 13 · see citing works
Fusion-Vital: Video-RF Fusion Transformer for Advanced Remote Physiological Measurement
Choi, J.-H., Kang, K.-B., & Kim, K. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i2.27898 · Cited by 13 · see citing works
Torbarina, L., Ferkovic, T., Roguski, L., Mihelčić, V., Šarlija, B., & Kraljevic, Z. (2023) · arXiv.org · doi:10.48550/arxiv.2308.08234 · Cited by 11 · see citing works
Multi‐vehicle multi‐sensor occupancy grid map fusion in vehicular networks
Meng, X., Duan, D., & Feng, T. (2021) · IET Communications · doi:10.1049/cmu2.12314 · Cited by 10 · see citing works
Weather and Meteorological Optical Range Classification for Autonomous Driving
Pereira, C., Cruz, R., Fernandes, J. N. D., Pinto, J. R., & Cardoso, J. S. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3387113 · Cited by 10 · see citing works
Multi-sensor fusion 3D object detection for autonomous driving
Alaba, S. Y., & Ball, J. E. (2023) · Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure · doi:10.1117/12.2663424 · Cited by 9 · see citing works
Multi-sensor data fusion using deep learning for bulky waste image classification
Bihler, M., Roming, L., Jiang, Y., Afifi, A. J., Aderhold, J., Cibiraite-Lukenskiene, D., Lorenz, S., Gloaguen, R., Gruna, R., & Heizmann, M. (2023) · Automated Visual Inspection and Machine Vision · doi:10.1117/12.2673838 · Cited by 8 · see citing works
Defense against Membership Inference Attack Applying Domain Adaptation with Addictive Noise
Huang, H. (2021) · Journal of Computer and Communications · doi:10.4236/jcc.2021.95007 · Cited by 7 · see citing works
Bounding Box Disparity: 3D Metrics for Object Detection with Full Degree of Freedom
Adam, M. G., Piccolrovazzi, M., Eger, S., & Steinbach, E. G. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897588 · Cited by 7 · see citing works
On the Performance and Memory Footprint of Distributed Training: An Empirical Study on Transformers
Lu, Z., Wang, F., Xu, Z., Yang, F., & Li, T. (2025) · Software Practice and Experience · doi:10.1002/spe.3421 · Cited by 5 · see citing works
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
Botev, A., De, S., Smith, S. L., Fernando, A., Muraru, G.-C., Haroun, R., Berrada, L., Pascanu, R., Sessa, P. G., Dadashi, R., et al. (2024) · arXiv · doi:10.48550/arxiv.2404.07839 · Cited by 5 · see citing works
Cooperative Connected Smart Road Infrastructure and Autonomous Vehicles for Safe Driving
Tang, Z., He, J., Flanagan, S. K., Procter, P., & Cheng, L. (2021) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp52444.2021.9651941 · Cited by 4 · see citing works
Abdullahi, A.-A., & Nazeh, M. (2023) · Advances in Computer and Communication · doi:10.26855/acc.2023.10.002 · Cited by 4 · see citing works
FrameQuant: Flexible Low-Bit Quantization for TransformersPreprint
Adepu, H., Zeng, Z., Zhang, L., & Singh, V. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2403.06082 · Cited by 4 · see citing works
CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation
Sun, L., Ding, G., Yoshiyasu, Y., & Kanehiro, F. (2022) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio55434.2022.10011808 · Cited by 3 · see citing works
Intelligent Fault Diagnosis of Photoelectric Pod Bearing Based on Multi-information Fusion
Wang, C., Liu, Z., & Wang, Y. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2136/1/012036 · Cited by 2 · see citing works
Deep 4D Automotive Radar-Camera Fusion Odometry with Cross-Modal Transformer Fusion
Lu, S., Zhuo, G., Xiong, L., Zhou, M., & Lu, X. (2023) · SAE International Journal of Advances and Current Practices in Mobility · doi:10.4271/2023-01-7040 · Cited by 2 · see citing works
Sensor Aided Beamforming in Vehicular Environment
Tan, M. C., Li, M., Abbasi, Q. H., & Imran, M. A. (2020) · International Conference on UK-China Emerging Technologies (UCET) · doi:10.1109/ucet51115.2020.9205411 · Cited by 1 · see citing works
Research on traffic object recognition based on multi-sensor fusion
Wu, W., & Liu, C. (2024) · International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID) · doi:10.1117/12.3026289 · Cited by 1 · see citing works
Flagged first, then by citations
2 of the 101 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Multimodal fusion and vision–language models: A survey for robot vision
Han, X., Chen, S., Fu, Z., Feng, Z., Fan, L., An, D., Wang, C., Guo, L., Meng, W., Zhang, X., et al. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103652 · Cited by 41 · see citing works
“…✗ ✗ ✗ Li et al. [29] 1 ✓ ✗ ✗ Wu et al. [30] 1 ✗ ✗ ✗ Bordes et al. [31] 1 ✓ ✓ ✗ Lin et al. [32] 1 ✓ ✗ ✗ Chen et al. [33] 1 ✗ ✗ ✗ Abdulmaksoud et al. [34] 2 Although recent years have seen several surveys on multimodal fusion and vision-language models, significant limitations remain in terms of…”
Collaborative Perception Datasets for Autonomous Driving: A Review
Wang, N. Y., Shang, D., Gong, Y., Hu, X., Song, Z., Yang, L., Huang, Y., Wang, X., & Lu, J. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3582040 · Cited by 23 · see citing works
Mansouri, M. M., Attouri, K., & Refaat, S. S. (2025) · IEEE Access · doi:10.1109/access.2025.3602914 · Cited by 19 · see citing works
Advancements in robotic positioning: A comprehensive review of measurement methods and systems
Haq, I. U., Felicetti, F., Carnì, D. L., & Lamonaca, F. (2026) · Measurement Sensors · doi:10.1016/j.measen.2026.102000 · Cited by 5 · see citing works
Aldossary, M. (2025) · Mathematics · doi:10.3390/math13183051 · Cited by 5 · see citing works
Lightweight Feature-Based Attention Network for Li-Ion Battery SOC Estimation
Abdulmaksoud, A., & Ahmed, R. (2025) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec63604.2025.11097926 · Cited by 2 · see citing works
Self-citationMockey, A. M. Y., Xia, K., Perera, P. L., & Wang, T. (2026) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-01085-y · Cited by 2 · see citing works
Gau, Y.-S., & Tseng, S. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132621 · Cited by 2 · see citing works
Stefanov, S., Markova, V. I., & Markov, M. (2025) · International Conference on Communications, Information, Electronic and Energy Systems (CIEES) · doi:10.1109/ciees66347.2025.11300010 · Cited by 1 · see citing works
Sæther, S. R., Sande, A. B., Seter, H., & Arnesen, P. (2026) · European transport studies. · doi:10.1016/j.ets.2026.100064 · Cited by 1 · see citing works
Dynamic Cascade Detector for Storage Tanks and Ships in Optical Remote Sensing Images
Wang, T., Liu, B., & Chen, P. (2025) · Remote Sensing · doi:10.3390/rs17111882
Technical Analysis: Generative AI Applications in Autonomous Vehicle Training for Adverse Conditions
Acharya, R. (2025) · European Journal of Computer Science and Information Technology · doi:10.37745/ejcsit.2013/vol13n398492
Electrical Systems in Autonomous Vehicles: A Comprehensive Study
Algburi, M. Q., Muhi, A. N., & Ghanim, M. (2025) · International Research Journal of Innovations in Engineering and Technology · doi:10.47001/irjiet/2025.912022
FPGA-Based MPSoCs for High-Performance Sensor Fusion: Accelerating Covariance Intersection
Sharf, H. M., & Hassan, M. G. (2025) · International Conference on Field Programmable Logic and Applications (FPL) · doi:10.1109/fpl68686.2025.00027
Reddy, N. K. S., Sudhakar, M., Karthikeyan, R., Sarangi, P. K., & Jabbar, M. A. (2025) · International Conference on Emerging Computation and Information Technologies (ICECIT) · doi:10.1109/icecit67774.2025.11451020
Autonomous Vehicle Navigation Using IoT Sensors and Deep Learning for Obstacle Detection
Zhen, Y., Ye, D.-W., Qi, D., & Cao, B.-X. (2026) · International Journal of Computational Intelligence and Applications · doi:10.1142/s1469026826410129
ArchSnoop: LLM Architecture Snooping via Electromagnetic Side-Channel on Edge Devices
Weng, H., Zhou, R., Qu, Y., Ji, X., & Xu, W. (2026) · ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) · doi:10.1145/3765613.3811673
Robust Decision-Level Fusion for Multimodal Fall Detection Under Controlled Sensor Noise
Taramasco, C., Ormeño-Arriagada, P., Costa, E., Andrade, R. M. D. C., & Araya, D. (2026) · IEEE Access · doi:10.1109/access.2026.3700011
Multi-Sensor Fusion With Transformer-based Deep Learning for Real-Time Fetal Health Monitoring
Packiavathy, V. S., & Davamani, K. A. (2026) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei68323.2026.11541436
Lyu, N., Zhao, J. X., Liu, P., Li, L., Xu, J., Wang, M., Zhang, Y., & Zheng, Y. (2026) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070261457839
Prajapati, M., Rai, S., Khan, M. S., & Silvestri, S. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6945154
FPGA Deployment of Extremely Quantized Models for Time-to-Lane-Change Prediction
Saad, A., Berta, R., Fresta, M., Lazzaroni, L., Pighetti, A., Srour, O., Tango, F., & Bellotti, F. (2026) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2026.3705103
Jain, A., Yadav, V. K., & Khatter, H. (2026) · Journal of Innovative Image Processing · doi:10.36548/jiip.2026.3.003
Computer Vision-Based Part Defect Detection
Wang, F. J. (2026) · International Conference on Signal Image Processing and Communication (ICSIPC) · doi:10.1109/icsipc69751.2026.11584245
FPGA-based Sensor Fusion for Autonomous Vehicles: A Multi-Sensor Integration Approach
Niranjan, P., Shubha, B., Nayak, N. N., K, P. N., Mascarenhas, R. A., & Poojary, R. K. (2026) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc70182.2026.11686928
Multi-LiDAR Fusion Perception System with Point-Wise Labeling for Electric Wheelchairs
Liu, T., Sekiguchi, K., & Nonaka, K. (2026) · Asian Control Conference (ASCC) · doi:10.1109/ascc70886.2026.11701879
Dodda, S., & Arunachalam, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-75748-x
Application of IoT Technology on a LiDAR-Based Robot for Navigation and Mapping
Adji, R. M. B., Christian, N., Azzaky, M. Z., & Widianto, M. H. (2026) · International Conference on Information Technology Research and Innovation (ICITRI) · doi:10.1109/icitri71999.2026.11751218
- Authors
- Abdulmaksoud, A., & Ahmed, R.
- Affiliations
- McMaster University (CA)
- Venue
- IEEE Access
- Published
- 2025
- Volume
- 13
- Pages
- 41822-41838
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4407900187
- Cited by
- 25
- References
- 101
- Citations by year
- 2026: 15 · 2025: 10
- FWCI
- 6.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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 › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- sensor fusion, transformer models, autonomous vehicles, LiDAR-camera fusion, radar-camera fusion, multimodal learning, severe weather, object detection, computational efficiency
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
