Multimodal vehicle detection: fusing 3D-LIDAR and color camera data

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works

You Only Look Once: Unified, Real-Time Object Detection

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works

Histograms of Oriented Gradients for Human Detection

Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works

Untitled work

(2016) · Cited by 31,590 · see citing works

Fast R-CNNPreprint

Girshick, R. B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.08083 · Cited by 29,416 · see citing works

YOLO9000: Better, Faster, Stronger

Redmon, J., & Farhadi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.690 · Cited by 19,892 · see citing works

Rapid object detection using a boosted cascade of simple features

Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300 · 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

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971 · see citing works

Fully convolutional networks for semantic segmentation

Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898 · see citing works

Backpropagation Applied to Handwritten Zip Code Recognition

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works

Object Detection with Discriminatively Trained Part-Based Models

Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853 · see citing works

Selective Search for Object Recognition

Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0620-5 · Cited by 5,980 · 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

Region-based convolutional networks for accurate object detection and segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2437384 · Cited by 2,912 · see citing works

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680 · see citing works

A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

Cai, Z., Fan, Q., Feris, R. S., & Vasconcelos, N. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_22 · Cited by 1,605 · see citing works

Autonomous driving in urban environments: Boss and the Urban Challenge

Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M. N., Dolan, J., Duggins, D., Galatali, T., Geyer, C., et al. (2008) · Journal of Field Robotics · doi:10.1002/rob.20255 · Cited by 1,555 · see citing works

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

Gupta, S., Girshick, R., Arbeláez, P., & Malik, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_23 · Cited by 1,528 · see citing works

Monocular 3D Object Detection for Autonomous Driving

Chen, X., Kundu, K., Zhang, Z., Ma, H., Fidler, S., & Urtasun, R. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.236 · Cited by 1,019 · see citing works

Making Bertha Drive—An Autonomous Journey on a Historic Route

Ziegler, J., Bender, P., Schreiber, M., Lategahn, H., Strauss, T., Stiller, C., Dang, T., Franke, U., Appenrodt, N., Keller, C. G., et al. (2014) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2014.2306552 · Cited by 854 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky (2012) · Medical Entomology and Zoology · Cited by 799 · see citing works

Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers

Yang, F., Choi, W., & Lin, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.234 · Cited by 608 · see citing works

Vehicle Detection from 3D Lidar Using Fully Convolutional Network

Li, B., Zhang, T., & Xia, T. (2016) · Robotics: Science and Systems XII · doi:10.15607/rss.2016.xii.042 · Cited by 577 · see citing works

3D fully convolutional network for vehicle detection in point cloud

Li, B. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8205955 · Cited by 488 · see citing works

Voting for Voting in Online Point Cloud Object Detection

Zeng Wang, D., & Posner, I. (2015) · Robotics: Science and Systems XI · doi:10.15607/rss.2015.xi.035 · Cited by 337 · see citing works

Data-driven 3D Voxel Patterns for object category recognition

Yu, X., Choi, W., Lin, Y., & Savarese, S. (2015) · 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.2015.7298800 · Cited by 319 · see citing works

Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection

Yu, X., Choi, W., Lin, Y., & Savarese, S. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.108 · Cited by 303 · see citing works

Vehicle Detection from 3D Lidar Using Fully Convolutional NetworkPreprint

Li, B., Zhang, T., & Xia, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.07916 · Cited by 275 · see citing works

Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art

Janai, J., Güney, F., Behl, A., & Geiger, A. (2020) · doi:10.1561/9781680836899 · Cited by 243 · see citing works

Multisensor Data Fusion

Durrant-Whyte, H. F., & Henderson, T. C. (2016) · Springer handbooks · doi:10.1007/978-3-319-32552-1_35 · Cited by 199 · see citing works

Pedestrian detection combining RGB and dense LIDAR data

Premebida, C., Carreira, J., Batista, J. P., & Nunes, U. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6943141 · Cited by 189 · see citing works

Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art

Janai, J., Güney, F., Behl, A., & Geiger, A. (2017) · arXiv · doi:10.48550/arxiv.1704.05519 · Cited by 141 · see citing works

Fusing LIDAR and images for pedestrian detection using convolutional neural networks

Schlosser, J., Chow, C. K., & Kira, Z. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487370 · Cited by 138 · see citing works

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

Oh, S.-I., & Kang, H.-B. (2017) · Sensors · doi:10.3390/s17010207 · Cited by 120 · see citing works

Sensor Fusion Methodology for Vehicle Detection

García, F., Martín, D., de la Escalera, A., & Armingol, J. M. (2017) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2016.2620398 · Cited by 110 · see citing works

On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts

González, A., Vázquez, D., López, A. M., & Amores, J. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2593940 · Cited by 108 · see citing works

Regionlets for Generic Object Detection

Wang, X., Yang, M., Zhu, S., & Lin, Y. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389830 · Cited by 107 · see citing works

Object Detection in Multi-view X-Ray Images

Franzel, T., Schmidt, U., & Roth, S. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32717-9_15 · Cited by 102 · see citing works

Laser-based segment classification using a mixture of bag-of-words

Behley, J., Steinhage, V., & Cremers, A. B. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696957 · Cited by 98 · see citing works

Multi-View and 3D Deformable Part Models

Pepik, B., Stark, M. M., Gehler, P., & Schiele, B. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2408347 · Cited by 81 · see citing works

High-resolution LIDAR-based depth mapping using bilateral filter

Premebida, C., Garrote, L., Asvadi, A., Ribeiro, A. P., & Nunes, U. (2016) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2016.7795953 · Cited by 68 · see citing works

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

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

Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues

Gupta, A., Anpalagan, A., Guan, L., & Khwaja, A. S. (2021) · Array · doi:10.1016/j.array.2021.100057 · Cited by 571 · 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

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

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works

A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

Ahangar, M. N., Ahmed, Q. Z., Khan, F. A., & Hafeez, M. (2021) · Sensors · doi:10.3390/s21030706 · Cited by 378 · see citing works

LIDAR–camera fusion for road detection using fully convolutional neural networks

Caltagirone, L., Bellone, M., Svensson, L., & Wahde, M. (2019) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.11.002 · Cited by 370 · see citing works

Fusion of 3D LIDAR and Camera Data for Object Detection in Autonomous Vehicle Applications

Zhao, X., Sun, P., Xu, Z., Min, H., & Yu, H. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2966034 · Cited by 320 · see citing works

Multimodal end-to-end autonomous driving

Xiao, Y., Codevilla, F., Gurram, A., Urfalioglu, O., & Lopez, A. M. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3013234 · Cited by 319 · see citing works

Deep multimodal fusion for semantic image segmentation: A survey

Zhang, Y., Sidibé, D., Morel, O., & Mériaudeau, F. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2020.104042 · Cited by 261 · see citing works

A Review of Vehicle Detection Techniques for Intelligent Vehicles

Wang, Z., Zhan, J., Duan, C., Guan, X., Lu, P., & Yang, K. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3128968 · Cited by 258 · see citing works

Recent Advances of Artificial Intelligence in Manufacturing Industrial Sectors: A Review

Kim, S. W., Kong, J. H., Lee, S. W., & Lee, S. (2021) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-021-00600-3 · Cited by 193 · see citing works

Multi-Modal 3D Object Detection in Autonomous Driving: A Survey

Wang, Y., Mao, Q., Zhu, H., Deng, J., Zhang, Y., Ji, J., Li, H., & Zhang, Y. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01784-z · Cited by 214 · see citing works

Multi-Sensor Fusion and Cooperative Perception for Autonomous Driving: A Review

Xiang, C. J., Chen, F., Xie, X., Shi, B., Lü, H., Lv, Y., Yang, M., & Niu, Z. (2023) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2023.3283864 · Cited by 187 · see citing works

A Survey of Driving Safety With Sensing, Vehicular Communications, and Artificial Intelligence-Based Collision Avoidance

Fu, Y., Li, C., Yu, F. R., Luan, T. H., & Zhang, Y. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3083927 · Cited by 169 · see citing works

Sensing and Machine Learning for Automotive Perception: A Review

Pandharipande, A., Cheng, C., Dauwels, J., Gürbüz, S. Z., Ibañez‐Guzmán, J., Li, G., Piazzoni, A., Wang, P., & Santra, A. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3262134 · Cited by 165 · see citing works

Enhancing vehicle detection in intelligent transportation systems via autonomous UAV platform and YOLOv8 integration

Bakırcı, M. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112015 · Cited by 153 · see citing works

Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines

Cheng, X., Zhang, H., Zhang, J., Gao, S., Li, S., Huang, Z., Bai, L., Yang, Z., Zheng, X., & Yang, L. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3336917 · Cited by 125 · see citing works

SelfVIO: Self-supervised deep monocular Visual–Inertial Odometry and depth estimation

Almalıoğlu, Y., Turan, M., Saputra, M. R. U., de Gusmão, P. P. B., Markham, A., & Trigoni, N. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.03.005 · Cited by 121 · see citing works

Survey of Datafusion Techniques for Laser and Vision Based Sensor Integration for Autonomous Navigation

Kolar, P., Benavidez, P. J., & Jamshidi, M. M. (2020) · Sensors · doi:10.3390/s20082180 · Cited by 112 · see citing works

A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles

Yu, X., & Marinov, M. V. (2020) · Sustainability · doi:10.3390/su12083281 · Cited by 110 · see citing works

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

Hu, L., Ou, J., Huang, J., Chen, Y., & Cao, D. (2020) · IEEE Access · doi:10.1109/access.2020.2970164 · Cited by 104 · see citing works

Application of Laser Systems for Detection and Ranging in the Modern Road Transportation and Maritime Sector

Lopac, N., Jurdana, I., Brnelić, A., & Krljan, T. (2022) · Sensors · doi:10.3390/s22165946 · Cited by 77 · see citing works

Multi-modal policy fusion for end-to-end autonomous driving

Huang, Z., Sun, S., Zhao, J., & Mao, L. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101834 · Cited by 74 · see citing works

Stabilization and Validation of 3D Object Position Using Multimodal Sensor Fusion and Semantic Segmentation

Muresan, M. P., Giosan, I., & Nedevschi, S. (2020) · Sensors · doi:10.3390/s20041110 · Cited by 73 · see citing works

Progress and Prospects of Multimodal Fusion Methods in Physical Human–Robot Interaction: A Review

Xue, T., Wang, W., Ma, J., Liu, W., Pan, Z., & Han, M. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2995271 · Cited by 74 · see citing works

A multimodal semantic segmentation for airport runway delineation in panchromatic remote sensing images

Datla, R., Chalavadi, V., & Chalavadi, K. M. (2022) · International Conference on Machine Vision · doi:10.1117/12.2622656 · Cited by 73 · see citing works

An insight into crash avoidance and overtaking advice systems for Autonomous Vehicles: A review, challenges and solutions

Perumal, P. S., Sujasree, M., Chavhan, S., Gupta, D. K., Mukthineni, V., Shimgekar, S. R., Khanna, A. K., & Fortino, G. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104406 · Cited by 73 · see citing works

Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring

Ahmad, U., Nasirahmadi, A., Hensel, O., & Marino, S. (2022) · Agronomy · doi:10.3390/agronomy12030555 · Cited by 65 · see citing works

Multi-Modal Fusion Technology Based on Vehicle Information: A Survey

Zhang, X., Gong, Y., Lu, J., Wu, J., Li, Z., Jin, D., & Li, J. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3268051 · Cited by 71 · see citing works

A Survey on Deep Learning Based Approaches for Scene Understanding in Autonomous Driving

Guo, Z., Huang, Y., Hu, X., Wei, H., & Zhao, B. (2021) · Electronics · doi:10.3390/electronics10040471 · Cited by 65 · see citing works

A one decade survey of autonomous mobile robot systems

Zghair, N. A. K., & Al-Araji, A. S. (2021) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v11i6.pp4891-4906 · Cited by 57 · see citing works

Target Fusion Detection of LiDAR and Camera Based on the Improved YOLO Algorithm

Han, J., Liao, Y., Zhang, J., Wang, S., & Li, S. (2018) · Mathematics · doi:10.3390/math6100213 · Cited by 56 · see citing works

Intelligent Robotic Perception Systems

Premebida, C., Ambrus, R., & Marton, Z.-C. (2019) · Applications of Mobile Robots · doi:10.5772/intechopen.79742 · Cited by 52 · see citing works

RI-Fusion: 3D Object Detection Using Enhanced Point Features With Range-Image Fusion for Autonomous Driving

Zhang, X., Wang, L., Zhang, G., Lan, T., Zhang, H., Zhao, L., Li, J., Zhu, L., & Liu, H. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3224525 · Cited by 49 · see citing works

A Survey of ADAS Perceptions With Development in China

Li, X., Lin, K., Meng, M., Li, X., Li, L., Hong, Y., & Chen, J. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3149763 · Cited by 47 · see citing works

Crisis-DIAS: Towards Multimodal Damage Analysis - Deployment, Challenges and Assessment

Agarwal, M., Leekha, M., Sawhney, R., & Shah, R. R. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5369 · Cited by 44 · see citing works

Accurate Classification for Automatic Vehicle-Type Recognition Based on Ensemble Classifiers

Shvai, N., Hasnat, A., Meicler, A., & Nakib, A. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2906821 · Cited by 44 · see citing works

Leveraging Thermal Imaging for Autonomous Driving

Miethig, B., Liu, A., Habibi, S. R., & Mohrenschildt, M. V. (2019) · IEEE Transportation Electrification Conference and Expo · doi:10.1109/itec.2019.8790493 · Cited by 44 · see citing works

Multimodal obstacle detection in unstructured environments with conditional random fields

Kragh, M., & Underwood, J. (2019) · Journal of Field Robotics · doi:10.1002/rob.21866 · Cited by 41 · see citing works

Sensor Data Fusion for a Mobile Robot Using Neural Networks

Barreto-Cubero, A. J., Gómez-Espinosa, A., Cabello, J. A. E., Cuan‐Urquizo, E., & Cruz-Ramírez, S. R. (2021) · Sensors · doi:10.3390/s22010305 · Cited by 40 · see citing works

Survey on Image and Point-Cloud Fusion-Based Object Detection in Autonomous Vehicles

Peng, Y., Qin, Y., Tang, X., Zhang, Z., & Deng, L. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3206235 · Cited by 39 · see citing works

Multi-Modal Obstacle Detection in Unstructured Environments with Conditional Random FieldsPreprint

Kragh, M. F., & Underwood, J. (2017) · Cited by 39 · see citing works

RangeLVDet: Boosting 3D Object Detection in LIDAR With Range Image and RGB Image

Zhang, Z., Liang, Z., Zhang, M., Zhao, X. F., Li, H., Yang, M. Z., Tan, W., & Pu, S. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3127626 · Cited by 39 · see citing works

A Review of Deep Learning-Based LiDAR and Camera Extrinsic Calibration

Tan, Z., Zhang, X., Teng, S., Wang, L., & Gao, F. (2024) · Sensors · doi:10.3390/s24123878 · Cited by 37 · see citing works

Multi-scale multi-modal fusion for object detection in autonomous driving based on selective kernel

Gao, X., Zhang, G., & Xiong, Y. (2022) · Measurement · doi:10.1016/j.measurement.2022.111001 · Cited by 35 · see citing works

Local binary pattern-based on-road vehicle detection in urban traffic scene

Hassaballah, M., Kenk, M. A., & Elhenawy, I. (2020) · Pattern Analysis and Applications · doi:10.1007/s10044-020-00874-9 · Cited by 32 · see citing works

From generalized zero-shot learning to long-tail with class descriptors

Samuel, D., Atzmon, Y., & Chechik, G. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00033 · Cited by 32 · see citing works

Improving exchange rate forecasting via a new deep multimodal fusion model

Windsor, E., & Cao, W. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03342-5 · Cited by 32 · see citing works

Deep learning-based fusion networks with high-order attention mechanism for 3D object detection in autonomous driving scenarios

Jiang, H., Lu, Y., Zhang, D., Shi, Y., & Wang, J. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111253 · Cited by 33 · see citing works

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Dong, Y., Fan, G., Zhou, Z., Liu, J., Wang, Y., & Chen, F. (2021) · Forests · doi:10.3390/f12081020 · Cited by 31 · see citing works

Last Decade in Vehicle Detection and Classification: A Comprehensive Survey

Maity, S., Bhattacharyya, A., Singh, P. K., Kumar, M., & Sarkar, R. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09764-1 · Cited by 31 · see citing works

Applications of Mobile Robots

Hurtado, E. G. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.74181 · Cited by 31 · see citing works

Analyzing Factors Influencing Situation Awareness in Autonomous Vehicles—A Survey

Ignatious, H. A., El‐Sayed, H., Khan, M. A., & Mokhtar, B. M. (2023) · Sensors · doi:10.3390/s23084075 · Cited by 30 · see citing works

Channel Attention in LiDAR-camera Fusion for Lane Line Segmentation

Zhang, X., Li, Z., Gao, X., Jin, D., & Li, J. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108020 · Cited by 30 · see citing works

Integrating Multi-Modal Sensors: A Review of Fusion Techniques for Intelligent Vehicles

Wei, C., Qin, Z., Zhang, Z., Wu, G., & Barth, M. (2025) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv64158.2025.11097396 · Cited by 35 · see citing works

Applying Deep Learning to Autonomous Vehicles: A Survey

Jing Ren, A., Gaber, H. A., & Jabar, S. S. A. (2021) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd51990.2021.9458968 · Cited by 26 · see citing works

Real-Time Vehicle Object Detection Method Based on Multi-Scale Feature Fusion

Guo, K., Li, X., Zhang, M., Bao, Q., & Yang, M. (2021) · IEEE Access · doi:10.1109/access.2021.3104849 · Cited by 26 · see citing works

Strym: A Python Package for Real-time CAN Data Logging, Analysis and Visualization to Work with USB-CAN Interface

Bhadani, R. K., Bunting, M., Nice, M. W., Tran, N. M., Elmadani, S., Work, D. B., & Sprinkle, J. M. (2022) · doi:10.1109/di-cps56137.2022.00009 · Cited by 26 · see citing works

Intelligent advice system for human drivers to prevent overtaking accidents in roads

Perumal, P. S., Wang, Y., Sujasree, M., Mukthineni, V., & Shimgekar, S. R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117178 · Cited by 25 · see citing works

RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground Surveillance

Kim, J., & Cho, J. (2021) · Sensors · doi:10.3390/s21051677 · Cited by 24 · see citing works

Automatic vehicle detection system in Day and Night Mode: challenges, applications and panoramic review

Arora, N., & Kumar, Y. (2022) · Evolutionary Intelligence · doi:10.1007/s12065-022-00723-0 · Cited by 24 · see citing works

Exploiting high-fidelity kinematic information from port surveillance videos via a YOLO-based framework

Xu, X., Chen, X., Wu, B., Wang, Z., & Zhen, J. (2022) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2022.106117 · Cited by 24 · see citing works

Usage of IR Sensors in the HVAC Systems, Vehicle and Manufacturing Industries: A Review

Altaf, M. A., Ahn, J., Khan, D., & Kim, M. Y. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3166190 · Cited by 23 · see citing works

Real-Time Monocular Obstacle Detection Based on Horizon Line and Saliency Estimation for Unmanned Surface Vehicles

Liu, J., Li, H., Liu, J., Xie, S., & Luo, J. (2021) · Mobile Networks and Applications · doi:10.1007/s11036-021-01752-2 · Cited by 22 · see citing works

Occupancy Grid Generation With Dynamic Obstacle Segmentation in Stereo Images

Shepel, I. O., Adeshkin, V., Belkin, I., & Yudin, D. A. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3133799 · Cited by 22 · see citing works

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Liu, J., & Zhang, R. (2020) · Journal of Sensors · doi:10.1155/2020/4372847 · Cited by 22 · see citing works

General-Purpose Deep Learning Detection and Segmentation Models for Images from a Lidar-Based Camera Sensor

Yu, X., Salimpour, S., Queralta, J. P., & Westerlund, T. (2023) · Sensors · doi:10.3390/s23062936 · Cited by 28 · see citing works

Enhancing Solid State LiDAR Mapping with a 2D Spinning LiDAR in Urban Scenario SLAM on Ground Vehicles

Wei, W., Shirinzadeh, B., Nowell, R., Ghafarian, M., Ammar, M. M. A., & Shen, T. (2021) · Sensors · doi:10.3390/s21051773 · Cited by 20 · see citing works

Intelligent Detection for Tunnel Shotcrete Spray Using Deep Learning and LiDAR

Chun-lei, L., Hao, S., Chun-Lai, L., & Li, J. (2019) · IEEE Access · doi:10.1109/access.2019.2962496 · Cited by 20 · see citing works

Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles

Khatab, E., Onsy, A., & Abouelfarag, A. A. (2022) · Sensors · doi:10.3390/s22041663 · Cited by 20 · see citing works

Deep representation learning for road detection using Siamese network

Liu, H., Han, X., Li, X., Yao, Y., Huang, P., & Tang, Z. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6986-1 · Cited by 19 · see citing works

AO-UOD: A Novel Paradigm for Underwater Object Detection Using Acousto–Optic Fusion

Yu, F., Xiao, F., Li, C., Cheng, E., & Yuan, F. (2025) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2025.3529121 · Cited by 18 · see citing works

A Survey of 3D Point Cloud and Deep Learning-Based Approaches for Scene Understanding in Autonomous Driving

Wang, L., & Huang, Y. (2021) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2021.3109041 · Cited by 19 · see citing works

Advance generalization technique through 3D CNN to overcome the false positives pedestrian in autonomous vehicles

Iftikhar, S., Asim, M., Zhang, Z., & El‐Latif, A. A. A. (2022) · Telecommunication Systems · doi:10.1007/s11235-022-00930-1 · Cited by 18 · see citing works

Road segmentation with image-LiDAR data fusion in deep neural network

Liu, H., Yao, Y., Sun, Z., Li, X., Jia, K., & Tang, Z. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07870-0 · Cited by 18 · see citing works

Multi-Modal 3D Object Detection in Autonomous Driving: a SurveyPreprint

Wang, Y., Mao, Q., Zhu, H., Deng, J., Zhang, Y., Ji, J., Li, H., & Zhang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.12735 · Cited by 18 · see citing works

Independent Biaxial Scanning Light Detection and Ranging System Based on Coded Laser Pulses without Idle Listening Time

Kim, G., & Park, Y. (2018) · Sensors · doi:10.3390/s18092943 · Cited by 17 · see citing works

Dynamic Scene’s Laser Localization by NeuroIV-Based Moving Objects Detection and LiDAR Points Evaluation

Liu, X., Yang, Z.-X., Hou, J. C., & Huang, W. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3184962 · Cited by 17 · see citing works

Improved Shape-Based Distance Method for Correlation Analysis of Multi-Radar Data Fusion in Self-Driving Vehicle

Ren, M., He, P. X., & Zhou, J. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3116057 · Cited by 16 · see citing works

CALNet: LiDAR-Camera Online Calibration With Channel Attention and Liquid Time-Constant Network

Shang, H., & Hu, B. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956145 · Cited by 16 · see citing works

MENet: Multi-Modal Mapping Enhancement Network for 3D Object Detection in Autonomous Driving

Liu, M., Chen, Y., Xie, J., Zhu, Y., Zhang, Y., Yao, L., Bing, Z., Zhuang, G., Huang, K., & Zhou, J. T. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3387398 · Cited by 16 · see citing works

Attention-based fusion network for RGB-D semantic segmentation

Li, Z., Guo, C., Zhan, J., & Deng, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128371 · Cited by 16 · see citing works

RoadSegNet: a deep learning framework for autonomous urban road detection

Pal, K., Yadav, P., & Katal, N. (2022) · Journal of Engineering and Applied Science · doi:10.1186/s44147-022-00162-9 · Cited by 15 · see citing works

Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approaches

Cunha, N., Barros, T., Reis, M., Marta, T., Premebida, C., & Nunes, U. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-59167-9_26 · Cited by 15 · see citing works

On the Road to AIoT: A Framework for Vehicle Road Cooperation

Annunziata, D., Chiaro, D., Qi, P., & Piccialli, F. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3488855 · Cited by 15 · see citing works

LIDAR Scanning as an Advanced Technology in Physical Hydraulic Modelling: The Stilling Basin Example

Li, R., Splinter, K. D., & Felder, S. (2021) · Remote Sensing · doi:10.3390/rs13183599 · Cited by 17 · see citing works

DAPnet: A Double Self-Attention Convolutional Network for Point Cloud Semantic Labeling

Chen, L., Chen, W., Xu, Z., Huang, H., Wang, S., Zhu, Q., & Li, H. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3113047 · Cited by 15 · see citing works

Enhancing Object Detection in Adverse Weather Conditions Through Entropy and Guided Multimodal Fusion

Zhang, Z., Gong, H., Feng, Y., Chu, Z., & Liu, H. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0972-7_2 · Cited by 14 · see citing works

Cross-Domain Few-Shot Micro-Expression Recognition Incorporating Action Units

Dai, Y. Q., & Feng, L. (2021) · IEEE Access · doi:10.1109/access.2021.3120542 · Cited by 14 · see citing works

Sensor Fusion for Aircraft Detection at Airport Ramps Using Conditional Random Fields

Lee, S., & Seo, S. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3157809 · Cited by 14 · see citing works

New Vehicle Classification Method Based on Hybrid Classifiers

Hasnat, A., Shvai, N., Meicler, A., Maarek, P., & Nakib, A. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451642 · Cited by 14 · see citing works

Smart Building Surveillance System as Shared Sensory System for Localization of AGVs

Oščádal, P., Huczala, D., Bém, J., Krys, V., & Bobovský, Z. (2020) · Applied Sciences · doi:10.3390/app10238452 · Cited by 14 · see citing works

A review of research on vehicle detection in adverse weather environments

Feng, S., Cai, X., Li, L., Wang, W., & Shi, Y. (2025) · Journal of Traffic and Transportation Engineering (English Edition) · doi:10.1016/j.jtte.2025.06.001 · Cited by 16 · see citing works

Target Detection for Construction Machinery Based on Deep Learning and Multisource Data Fusion

Wang, Y., Liu, X., Zhao, Q., He, H., & Yao, Z. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3264526 · Cited by 13 · see citing works

Late-Fusion Multimodal Human Detection Based on RGB and Thermal Images for Robotic Perception

Sousa, E., Mota, K. O. S., Gomes, I. P., Garrote, L., Wolf, D. F., & Premebida, C. (2023) · European Conference on Mobile Robots · doi:10.1109/ecmr59166.2023.10256301 · Cited by 13 · see citing works

Multi-DisNet: Machine Learning-Based Object Distance Estimation from Multiple Cameras

Abdul, H. M., Ristić–Durrant, D., Gräser, A., Banić, M., & Stamenković, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34995-0_41 · Cited by 13 · see citing works

Point Cloud Compression: Impact on Object Detection in Outdoor Contexts

Garrote, L., Perdiz, J., Cruz, L. A. D. S., & Nunes, U. (2022) · Sensors · doi:10.3390/s22155767 · Cited by 12 · see citing works

Multi-Camera 3D Object Detection for Autonomous Driving Using Deep Learning and Self-Attention Mechanism

Hazarika, A., Vyas, A., Rahmati, M., & Wang, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3288112 · Cited by 13 · see citing works

RGB-D Object Classification for Autonomous Driving Perception

Premebida, C., Melotti, G., & Asvadi, A. (2019) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-28603-3_17 · Cited by 12 · see citing works

Multimodal Multi-Sensor Camera-LiDAR Fusion for 3D Object Detection in Autonomous Vehicles

Lamichhane, B. R., Horanont, T., Srijuntongsiri, G., Niamla-ong, N., Somset, N., & Sen, C. S. (2025) · International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) · doi:10.1109/isai-nlp66160.2025.11320775

LiDAR-camera fusion for multi-modal 3D object detection

Liu, C., Yang, H., Kao, W., & Bai, J. (2023) · International Conference on Advanced Algorithms and Neural Networks (AANN) · doi:10.1117/12.3004702

Deep learning multi-modal fusion based 3D object detection

Zhang, H. (2023) · theses.fr (ABES) · doi:10.70675/b9b489ffz335dz4ff4zbb03z08b285d48435

UVFusion: A Unified Framework for Voxel-Aligned LiDAR–Camera Fusion in 3-D Object Detection

Liu, A., Wang, J., Chen, J. F., & Meng, Z. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3689658

3D Vehicle Detection Algorithm Based on Multimodal Decision-Level Fusion

Shi, P., Qi, H., Liu, Z.-Q., & Yang, A. (2022) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.022304 · Cited by 2 · see citing works