{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T14:17:14Z","timestamp":1783693034922,"version":"3.55.0"},"reference-count":163,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Pedestrian detection is a fundamental component of Advanced Driver Assistance Systems (ADAS) and plays a key role in collision avoidance and the safety of vulnerable road users. This paper presents a structured review of pedestrian detection methodologies developed between 2000 and 2025, spanning classical vision techniques and modern deep learning architectures. We organize the review into two phases. First, we examine classical methods, including Histogram of Oriented Gradients (HOG)+Support Vector Machine (SVM), Viola\u2013Jones, Deformable Part Models, and Integral Channel Features, which established the conceptual foundations of the field. Then, we analyze state-of-the-art deep learning architectures, categorized by detector stage (one-stage vs. two-stage), localization strategy (anchor-based vs. anchor-free), feature extraction paradigm (Convolutional Neural Network (CNN)-based vs. transformer-based), output representation (bounding box vs. instance segmentation), and computational profile (lightweight vs. heavyweight). Several design principles introduced by classical methods remain visible in modern architectures, indicating that they were not fully superseded. The review also examines publicly available benchmark datasets and compares the strengths and limitations of camera-, Light Detection And Ranging (LiDAR)-, radar-, and multi-sensor-fusion-based systems for ADAS deployment. We close by identifying six open problems for the field: adversarial robustness, real-time inference under embedded constraints, detection under adverse weather, dataset bias and demographic fairness, the deployment of Bird\u2019s-Eye View (BEV) and unified perception on automotive hardware, and explainability for safety-critical use.<\/jats:p>","DOI":"10.3390\/jimaging12070317","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T13:16:28Z","timestamp":1783689388000},"page":"317","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pedestrian Detection Techniques for Advanced Driver Assistance Systems: A Comprehensive Review"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8434-5222","authenticated-orcid":false,"given":"D\u0103nu\u0163-Ovidiu","family":"Pop","sequence":"first","affiliation":[{"name":"Electrical Engineering and Information Technology Department, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 T\u00e2rgu Mure\u015f, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2246-3415","authenticated-orcid":false,"given":"Adrian-Silviu","family":"Roman","sequence":"additional","affiliation":[{"name":"Electrical Engineering and Information Technology Department, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 T\u00e2rgu Mure\u015f, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rafe, A., and Singleton, P.A. (2024, January 15\u201318). Exploring the determinants of pedestrian crash severity using an AutoML approach. Proceedings of the International Conference on Transportation and Development 2024, Atlanta, GA, USA.","DOI":"10.1061\/9780784485514.039"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"149318","DOI":"10.1109\/ACCESS.2019.2944792","article-title":"Multi-Task Deep Learning for Pedestrian Detection, Action Recognition and Time to Cross Prediction","volume":"7","author":"Pop","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","unstructured":"World Health Organization (2023). Global Status Report on Road Safety 2023, World Health Organization. Technical Report."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"128529","DOI":"10.1016\/j.eswa.2025.128529","article-title":"Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey","volume":"292","author":"Silva","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4913","DOI":"10.1109\/TPAMI.2021.3076733","article-title":"From Handcrafted to Deep Features for Pedestrian Detection: A Survey","volume":"44","author":"Cao","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","unstructured":"Zhang, S., Shi, Y., Wang, B., Zhang, Y., Karim, M.M., Chen, K.P., Liu, C., Nasri, M., and Wang, Y. (2025). A Comprehensive Review on Artificial Intelligence Empowered Solutions for Enhancing Pedestrian and Cyclist Safety. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105106","DOI":"10.1016\/j.imavis.2024.105106","article-title":"Pedestrian detection in low-light conditions: A comprehensive survey","volume":"148","author":"Ghari","year":"2024","journal-title":"Image Vis. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sui, H.G., Han, H., Cui, Y., Yang, M., and Pei, B. (2025). FA-YOLO: A Pedestrian Detection Algorithm with Feature Enhancement and Adaptive Sparse Self-Attention. Electronics, 14.","DOI":"10.3390\/electronics14091713"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Neha, F., Bhati, D., Shukla, D.K., and Amiruzzaman, M. (2024). From classical techniques to convolution-based models: A review of object detection algorithms. arXiv.","DOI":"10.1109\/IPAS63548.2025.10924494"},{"key":"ref_13","unstructured":"Vachhani, H., Akilan, T., Devmurari, Y., Shaik, N., and Patel, D. (2023). Low-light Pedestrian Detection in Visible and Infrared Image Feeds: Issues and Challenges. arXiv."},{"key":"ref_14","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Lake Tahoe, NV, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24\u201327). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2655","DOI":"10.1109\/TIFS.2020.2970590","article-title":"PurifyNet: A Robust Person Re-Identification Model With Noisy Labels","volume":"15","author":"Ye","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Van Gool, L. (2006). Surf: Speeded up robust features. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/11744023_32"},{"key":"ref_20","first-page":"886","article-title":"Histograms of Oriented Gradients for Human Detection","volume":"Volume 1","author":"Dalal","year":"2005","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","article-title":"Robust Real-Time Face Detection","volume":"57","author":"Viola","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","article-title":"Fast Feature Pyramids for Object Detection","volume":"36","author":"Appel","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r, P., Belongie, S., and Perona, P. (2010). The Fastest Pedestrian Detector in the West. Proceedings of the British Machine Vision Conference (BMVC), BMVA Press.","DOI":"10.5244\/C.24.68"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., and Schiele, B. (2015). Filtered Channel Features for Pedestrian Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2015.7298784"},{"key":"ref_25","unstructured":"Nam, W., Doll\u00e1r, P., and Han, J.H. (2014, January 8\u201313). Local Decorrelation for Improved Pedestrian Detection. Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), Montreal, QC, Canada."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Felzenszwalb, P.F., Girshick, R.B., and McAllester, D. (2010). Cascade Object Detection with Deformable Part Models. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2010.5539906"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object Detection with Discriminatively Trained Part-Based Models","volume":"32","author":"Felzenszwalb","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Park, D., Ramanan, D., and Fowlkes, C. (2010). Multiresolution Models for Object Detection. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-642-15561-1_18"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yan, J., Zhang, X., Lei, Z., Liao, S., and Li, S.Z. (2013). Robust Multi-resolution Pedestrian Detection in Traffic Scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2013.390"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ouyang, W., and Wang, X. (2013). Joint Deep Learning for Pedestrian Detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2013.257"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ouyang, W., and Wang, X. (2013). Single-Pedestrian Detection Aided by Multi-pedestrian Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2013.411"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, L., Xu, L., and Yang, M.H. (2016). Pedestrian Detection in Crowded Scenes via Scale and Occlusion Analysis. Proceedings of the IEEE International Conference on Image Processing (ICIP), IEEE.","DOI":"10.1109\/ICIP.2016.7532550"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zeng, W., Jin, S., Qian, C., Luo, P., and Liu, W. (2024). When Pedestrian Detection Meets Multi-modal Learning: Generalist Model and Benchmark Dataset. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-031-73195-2_25"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xu, D., Ouyang, W., Ricci, E., Wang, X., and Sebe, N. (2017). Learning Cross-Modal Deep Representations for Robust Pedestrian Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2017.451"},{"key":"ref_35","first-page":"210","article-title":"Pedestrian Recognition Using Cross-Modality Learning in Convolutional Neural Networks","volume":"13","author":"Pop","year":"2021","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_36","first-page":"1244","article-title":"Attention-based Cross-Modality Multiscale Fusion for Multispectral Pedestrian Detection","volume":"14","author":"Zhou","year":"2023","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dou, H., Chen, S., Xu, F., Liu, Y.Y., and Zhao, H. (2025). Analysis of vehicle and pedestrian detection effects of improved YOLOv8 model in drone-assisted urban traffic monitoring system. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0314817"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"13801","DOI":"10.1109\/TITS.2023.3303320","article-title":"A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perception","volume":"24","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_40","first-page":"1","article-title":"Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey","volume":"58","author":"Zhou","year":"2025","journal-title":"Acm Comput. Surv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"9383","DOI":"10.1007\/s00500-023-08289-4","article-title":"Autonomous pedestrian detection for crowd surveillance using deep learning framework","volume":"27","author":"Thakur","year":"2023","journal-title":"Soft Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s11554-024-01519-4","article-title":"YOLO-TLA: An Efficient and Lightweight Small Object Detection Model based on YOLOv5","volume":"21","author":"Ji","year":"2024","journal-title":"J. -Real-Time Image Process."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yin, Y., Zhang, Z., Lin, W., Geng, C., Ran, H., and Zhu, H. (2023). Pedestrian detection algorithm integrating large kernel attention and YOLOV5 lightweight model. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0294865"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"13276","DOI":"10.1109\/TNNLS.2024.3443455","article-title":"TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection","volume":"36","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). SSD: Single Shot MultiBox Detector. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020). Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection. Proceedings of the 2020 IEEE\/CVF IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"109071","DOI":"10.1016\/j.patcog.2022.109071","article-title":"Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection","volume":"135","author":"Liu","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"18888","DOI":"10.1038\/s41598-024-69813-6","article-title":"Cosine similarity-guided knowledge distillation for robust object detectors","volume":"14","author":"Park","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4897","DOI":"10.1007\/s40747-024-01409-z","article-title":"Combining transformer global and local feature extraction for object detection","volume":"10","author":"Li","year":"2024","journal-title":"Complex Intell. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-End Object Detection with Transformers. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_51","unstructured":"Kartiman, F.N.N., Rasim, R., Wihardi, Y., Hasanah, N., Natan, O., Wahono, B., and Salim, T.I. (2025). SKGE-SWIN: End-To-End Autonomous Vehicle Waypoint Prediction and Navigation Using Skip Stage Swin Transformer. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Silva, D., Elsheikh, A.H., Smagulova, K., Fouda, M.E., and Eltawil, A.M. (2025). Chimera: A block-based neural architecture search framework for event-based object detection. Front. Artif. Intell., 8.","DOI":"10.3389\/frai.2025.1644889"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"44888","DOI":"10.1038\/s41598-025-28737-5","article-title":"Robustness analysis of YOLO and faster R-CNN for object detection in realistic weather scenarios with noise augmentation","volume":"15","author":"Gholinavaz","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_54","unstructured":"Wada, K., Kitagawa, S., Okada, K., and Inaba, M. (2020). Instance Segmentation of Visible and Occluded Regions for Finding and\\n Picking Target from a Pile of Objects. arXiv."},{"key":"ref_55","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2025, January 15). Ultralytics YOLOv8, YOLOv9 and YOLOv10 Documentation. Available online: https:\/\/docs.ultralytics.com."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Cheng, S., Chen, L., and Yang, K. (2025). DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles. Algorithms, 18.","DOI":"10.3390\/a18060358"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Qin, G., Pan, R., Deng, Y., Mi, P., and Zhu, Y. (2025). Road pedestrian detection and tracking algorithm based on improved YOLOv5s and DeepSORT. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0334786"},{"key":"ref_58","unstructured":"(2003). Detecting pedestrians using patterns of motion and appearance. Proceedings of the Ninth IEEE International Conference on Computer Vision, IEEE."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhang, S., Benenson, R., and Schiele, B. (2017). CityPersons: A Diverse Dataset for Pedestrian Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2017.474"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1007\/s11063-024-11558-4","article-title":"HF-YOLO: Advanced Pedestrian Detection Model with Feature Fusion and Imbalance Resolution","volume":"56","author":"Pan","year":"2024","journal-title":"Neural Process. Lett."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (2019). FCOS: Fully Convolutional One-Stage Object Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024). DETRs Beat YOLOs on Real-time Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_64","unstructured":"Lv, W., Zhao, Y., Chang, Q., Huang, K., Wang, G., and Liu, Y. (2024). Rt-detrv2: Improved baseline with bag-of-freebies for real-time detection transformer. arXiv."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Maya-Mart\u00ednez, S.U., Arg\u00fcelles-Cruz, A.J., Guzman-Zavaleta, Z.J., and de Jes\u00fas Ram\u00edrez-Cadena, M. (2023). Pedestrian detection model based on Tiny-Yolov3 architecture for wearable devices to visually impaired assistance. Front. Robot. AI, 10.","DOI":"10.3389\/frobt.2023.1052509"},{"key":"ref_66","unstructured":"Alfikri, M.D., and Kaliski, R. (2024). Real-Time Pedestrian Detection on IoT Edge Devices: A Lightweight Deep Learning Approach. arXiv."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Hwang, S., Park, J., Kim, N., Choi, Y., and Kweon, I.S. (2015). Multispectral pedestrian detection: Benchmark dataset and baseline. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2015.7298706"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zhao, R., Zhang, Z., Xu, Y., Yao, Y., Huang, Y., Zhang, W., Song, Z., Chen, X., and Zhao, Y. (2025). PedDet: Adaptive Spectral Optimization for Multimodal Pedestrian Detection. arXiv.","DOI":"10.3233\/FAIA250915"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"19937","DOI":"10.1109\/ACCESS.2022.3150988","article-title":"Occlusion Handling and Multi-Scale Pedestrian Detection Based on Deep Learning: A Review","volume":"10","author":"Li","year":"2022","journal-title":"IEEE Access"},{"key":"ref_70","unstructured":"Ouyang, W., and Wang, X. (2012). A Discriminative Deep Model for Pedestrian Detection with Occlusion Handling. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"129464","DOI":"10.1016\/j.eswa.2025.129464","article-title":"Insight any invisible: An occlusion perception method for robust pedestrian detection in crowded scenes","volume":"297","author":"Li","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Li, C., Wang, Y., and Liu, X. (2023). An Improved YOLOv7 Lightweight Detection Algorithm for Obscured Pedestrians. Sensors, 23.","DOI":"10.3390\/s23135912"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhou, Z., and Zhang, Z. (2025). RSO-YOLO: A Real-Time Detector for Small and Occluded Objects in Autonomous Driving Scenarios. Sensors, 25.","DOI":"10.3390\/s25216703"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_75","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as Points. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE."},{"key":"ref_76","unstructured":"Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_78","unstructured":"Hasan, I., Liao, S., Li, J., Akram, S.U., and Shao, L. (2022). Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond. arXiv."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Hasan, I., Liao, S., Li, J., Akram, S.U., and Shao, L. (2021). Generalizable Pedestrian Detection: The Elephant in the Room. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR46437.2021.01117"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s42154-025-00361-z","article-title":"A Survey of Human Intelligence Augmented Artificial Intelligence: An Autonomous Driving Perspective","volume":"8","author":"Li","year":"2025","journal-title":"Automot. Innov."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., and Shao, L. (2021). Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref_82","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention Is All You Need. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_83","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 3\u20137). An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale. Proceedings of the International Conference on Learning Representations, Virtual."},{"key":"ref_84","unstructured":"Child, R., Gray, S., Radford, A., and Sutskever, I. (2019). Generating Long Sequences with Sparse Transformers. arXiv."},{"key":"ref_85","unstructured":"Beltagy, I., Peters, M.E., and Cohan, A. (2020, January 5\u201310). Longformer: The Long-Document Transformer. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Seattle, DC, USA."},{"key":"ref_86","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2021, January 3\u20137). Deformable DETR: Deformable Transformers for End-to-End Object Detection. Proceedings of the International Conference on Learning Representations, Virtual."},{"key":"ref_87","unstructured":"Choromanski, K., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J.Q., Mohiuddin, A., and Kaiser, \u0141. (2021, January 3\u20138). Rethinking Attention with Performers. Proceedings of the International Conference on Learning Representations, Virtual."},{"key":"ref_88","unstructured":"Mehta, S., and Rastegari, M. (2022, January 25\u201329). MobileViT: Light-Weight, General-Purpose, and Mobile-Friendly Vision Transformer. Proceedings of the International Conference on Learning Representations, Virtual."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Saleh, K. (2022). Pedestrian Trajectory Prediction for Real-Time Autonomous Systems via Context-Augmented Transformer Networks. Sensors, 22.","DOI":"10.3390\/s22197495"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Tian, R., and Ding, Z. (2023, January 7\u201314). TrEP: Transformer-Based Evidential Prediction for Pedestrian Intention with Uncertainty. Proceedings of the AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence, Washinton, DC, USA.","DOI":"10.1609\/aaai.v37i3.25463"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s43067-023-00123-z","article-title":"Object detection using convolutional neural networks and transformer-based models: A review","volume":"10","author":"Shah","year":"2023","journal-title":"J. Electr. Syst. Inf. Technol."},{"key":"ref_92","first-page":"104558","article-title":"Comparative analysis of multiple YOLO-based target detectors and trackers for ADAS in edge devices","volume":"171","author":"Santos","year":"2023","journal-title":"Robot. Auton. Syst."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"19399","DOI":"10.1109\/TITS.2024.3447453","article-title":"Embedded Real-Time Vehicle and Pedestrian Detection Using a Compressed Tiny YOLO v3 Architecture","volume":"25","author":"Falaschetti","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., and Lee, Y.J. (2019). YOLACT: Real-Time Instance Segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2019.00925"},{"key":"ref_95","first-page":"17721","article-title":"SOLOv2: Dynamic and Fast Instance Segmentation","volume":"33","author":"Wang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst. (NeurIPS)"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Nguyen, H.N., Luong, T.N., Minh, T.P., Hong, N.M.T., Anh, K.T., Hong, Q.B., and Bach, N.P.V. (2025). Integration of Multi-Sensor Fusion and Decision-Making Architecture for Autonomous Vehicles in Multi-Object Traffic Conditions. Sensors, 25.","DOI":"10.20944\/preprints202510.0738.v1"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"9824","DOI":"10.1109\/TITS.2025.3570794","article-title":"PIP-Net: Pedestrian Intention Prediction in the Wild","volume":"26","author":"Azarmi","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3418213","article-title":"Bottom-up and Layerwise Domain Adaptation for Pedestrian Detection in Thermal Images","volume":"17","author":"Kieu","year":"2021","journal-title":"Acm Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., and Girdhar, R. (2022). Masked-attention Mask Transformer for Universal Image Segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"33692","DOI":"10.1038\/s41598-025-18845-7","article-title":"Contour-Based Instance Segmentation Method of Road Scene","volume":"15","author":"Wang","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Qiao, D., and Zulkernine, F. (2023). Adaptive Feature Fusion for Cooperative Perception Using LiDAR Point Clouds. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE.","DOI":"10.1109\/WACV56688.2023.00124"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.5755\/j01.itc.52.4.33766","article-title":"Research on Pedestrian Detection Based on Multimodal Infor-mation Fusion","volume":"52","author":"Yang","year":"2023","journal-title":"Inf. Technol. Control"},{"key":"ref_103","first-page":"117421","article-title":"Multi-Exposure Image Enhancement and Yolo Integration for Nighttime Pedestrian Detection","volume":"140","author":"Dai","year":"2025","journal-title":"Ssrn Electron. J."},{"key":"ref_104","first-page":"131","article-title":"Information Acquisition on Pedestrian Movements in Urban Traffic with a Mobile Multi-Sensor System","volume":"Volume XLIII-B2-2021","author":"Borgmann","year":"2021","journal-title":"International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Wei, C., Qin, Z., Zhang, Z., Wu, G., and Barth, M. (2025). Integrating Multi-Modal Sensors: A Review of Fusion Techniques for Intelligent Vehicles. Proceedings of the 2025 IEEE Intelligent Vehicles Symposium (IV), IEEE.","DOI":"10.1109\/IV64158.2025.11097396"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Jung, M., and Cho, J. (2024). Enhancing Detection of Pedestrians in Low-Light Conditions by Accentuating Gaussian-Sobel Edge Features from Depth Maps. Appl. Sci., 14.","DOI":"10.3390\/app14188326"},{"key":"ref_107","first-page":"194","article-title":"Wireless Sensor Network Based Real-Time Pedestrian Detection and Classification for Intelligent Transportation System","volume":"8","author":"Kumar","year":"2023","journal-title":"Int. J. Math. Eng. Manag. Sci."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Islam, M.M., Newaz, A.A.R., and Karimoddini, A. (2021). A Pedestrian Detection and Tracking Framework for Autonomous Cars: Efficient Fusion of Camera and LiDAR Data. Proceedings of the 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE.","DOI":"10.1109\/SMC52423.2021.9658639"},{"key":"ref_109","first-page":"295","article-title":"Pedestrian Safety\u2014Exploring the Different Applications for Pedestrian Sensing Detection","volume":"9","author":"Ullatil","year":"2020","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_110","first-page":"33","article-title":"Evaluation of Image Detection Techniques Acquired from Camera Images","volume":"9","author":"Erkartal","year":"2025","journal-title":"Int. J. Eng. Sci. Appl."},{"key":"ref_111","unstructured":"Bheemaiah, A., and Yang, S.Y. (2025). Knowledge Graphs as World Models for Semantic Material-Aware Obstacle Handling in Autonomous Vehicles. arXiv."},{"key":"ref_112","first-page":"657","article-title":"Deep Learning for Object Detection Using Radar Data","volume":"10","author":"Reda","year":"2023","journal-title":"Isprs Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.jsr.2020.03.005","article-title":"Understanding crashes involving roadway objects with SHRP 2 naturalistic driving study data","volume":"73","author":"Hao","year":"2020","journal-title":"J. Saf. Res."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/ITSC.2019.8917238","article-title":"People Tracking by Cooperative Fusion of RADAR and Camera Sensors","volume":"Volume 13","author":"Dimitrievski","year":"2019","journal-title":"Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC)"},{"key":"ref_115","first-page":"114220I","article-title":"HODET: Hybrid object detection and tracking using mmWave radar and visual sensors","volume":"Volume 11422","author":"Cyr","year":"2020","journal-title":"Sensors and Systems for Space Applications XIII"},{"key":"ref_116","unstructured":"Ding, S., Tang, Y., Vincenzi, M.D., and Suo, D. (2024). High and Low Resolution Tradeoffs in Roadside Multimodal Sensing. arXiv."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5194\/ars-10-45-2012","article-title":"Pedestrian recognition using automotive radar sensors","volume":"10","author":"Bartsch","year":"2012","journal-title":"Adv. Radio Sci."},{"key":"ref_118","unstructured":"Cai, K., Xia, Q., Li, P., Stankovic, J., and Lu, C.X. (2023). Robust Human Detection under Visual Degradation via Thermal and mmWave Radar Fusion. arXiv."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Yao, S., Guan, R., Huang, X., Li, Z., Sha, X., Yue, Y., Lim, E.G., Seo, H., Man, K.L., and Zhu, X. (2023). Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review. Proceedings of the IEEE Transactions on Intelligent Vehicles, IEEE.","DOI":"10.1109\/TIV.2023.3307157"},{"key":"ref_120","unstructured":"Barbosa, F.M., and Os\u00f3rio, F.S. (2023). Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics. arXiv."},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez, V.J.E., Schwarzl, C., and Josvai, S. (2020). Radar detection rate comparison through a mobile robot platform at the ZalaZONE proving ground. arXiv.","DOI":"10.1109\/ICRAE50850.2020.9310861"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Bhatt, N.P., Zhang, R., Ning, M., Alghooneh, A.R., Sun, J., Panahandeh, P., Mohammadbagher, E., Ecclestone, T., MacCallum, B., and Hashemi, E. (2023). WATonoBus: Field-Tested All-Weather Autonomous Shuttle Technology. arXiv.","DOI":"10.1109\/ITSC58415.2024.10919505"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"2769","DOI":"10.30574\/wjarr.2024.24.1.3076","article-title":"Adaptive 4D Radar and LiDAR Fusion for All-Weather 3D Object Detection in Autonomous Driving","volume":"24","author":"Nadendla","year":"2024","journal-title":"World J. Adv. Res. Rev."},{"key":"ref_124","unstructured":"Yang, L., Zheng, L., Ai, W., Liu, M., Li, S., Lin, Q., Yan, S., Bai, J., Ma, Z., and Zhu, X. (2025). MetaOcc: Surround-View 4D Radar and Camera Fusion Framework for 3D Occupancy Prediction with Dual Training Strategies. arXiv."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Zou, F., Deng, H.W., Iunn, T.U., Ogle, J., and Jin, W. (2023). V2V-based Collision-avoidance Decision Strategy for Autonomous Vehicles Interacting with Fully Occluded Pedestrians at Midblock on Multilane Roadways. arXiv.","DOI":"10.1061\/9780784484876.017"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Vora, S., Lang, A.H., Helou, B., and Beijbom, O. (2020). PointPainting: Sequential Fusion for 3D Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.00466"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Nabati, R., and Qi, H. (2021). CenterFusion: Center-Based Radar and Camera Fusion for 3D Object Detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, IEEE.","DOI":"10.1109\/WACV48630.2021.00157"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Prakash, A., Chitta, K., and Geiger, A. (2021). Multi-Modal Fusion Transformer for End-to-End Autonomous Driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR46437.2021.00700"},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Liu, Z., Tang, H., Amini, A., Yang, X., Mao, H., Rus, D.L., and Han, S. (2023). Bevfusion: Multi-task multi-sensor fusion with unified bird\u2019s-eye view representation. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA48891.2023.10160968"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Dai, Z., Wolf, A., Ley, P., Gl\u00fcck, T., Sundermeier, M., and Lachmayer, R. (2022). Requirements for Automotive LiDAR Systems. Sensors, 22.","DOI":"10.3390\/s22197532"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020). nuscenes: A multimodal dataset for autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Dollar, P., Wojek, C., Schiele, B., and Perona, P. (2009). Pedestrian Detection: A Benchmark. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, USA, 20\u201325 June 2009, IEEE.","DOI":"10.1109\/CVPR.2009.5206631"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016). The Cityscapes Dataset for Semantic Urban Scene Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012). Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.1109\/TPAMI.2019.2897684","article-title":"EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes","volume":"41","author":"Braun","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1109\/TMM.2019.2929005","article-title":"Widerperson: A diverse dataset for dense pedestrian detection in the wild","volume":"22","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez, A., Fang, Z., Socarras, Y., Serrat, J., V\u00e1zquez, D., Xu, J., and L\u00f3pez, A.M. (2016). Pedestrian Detection at Day\/Night Time with Visible and FIR Cameras: A Comparison. Sensors, 16.","DOI":"10.3390\/s16060820"},{"key":"ref_138","unstructured":"Weng, X., Man, Y., Park, J., Yuan, Y., O\u2019Toole, M., and Kitani, K.M. (2021). All-in-one drive: A comprehensive perception dataset with high-density long-range point clouds. arXiv."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Barnes, D., Gadd, M., Murcutt, P., Newman, P., and Posner, I. (2020). The oxford radar robotcar dataset: A radar extension to the oxford robotcar dataset. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA40945.2020.9196884"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Isik, M., Tiwari, K., Eryilmaz, M.B., and Dikmen, I.C. (2024). Accelerating Sensor Fusion in Neuromorphic Computing: A Case Study on Loihi-2. arXiv.","DOI":"10.1109\/HPEC62836.2024.10938501"},{"key":"ref_141","unstructured":"Wang, H., Nie, Y., Li, Y., Liu, H., Liu, M., Cheng, W., and Wang, Y. (2024). Research, Applications and Prospects of Event-Based Pedestrian Detection: A Survey. arXiv."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"4716","DOI":"10.1109\/TITS.2023.3248483","article-title":"Emergent Visual Sensors for Autonomous Vehicles","volume":"24","author":"Li","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Smagulova, K., Elsheikh, A.H., Silva, D., Fouda, M.E., and Eltawil, A.M. (2025). Efficient and real-time perception: A survey on end-to-end event-based object detection in autonomous driving. Front. Robot. AI, 12.","DOI":"10.3389\/frobt.2025.1674421"},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Musabini, A., Benmokhtar, R., Bhanushali, J., Galizzi, V., Luvison, B., and Perrotton, X. (2025). Valeo Near-Field: A novel dataset for pedestrian intent detection. arXiv.","DOI":"10.1109\/ICCVW69036.2025.00008"},{"key":"ref_145","unstructured":"Li, X., Chen, Z., Zhang, J.M., Sarro, F., Zhang, Y., and Liu, X. (2023). Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems. arXiv."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"15706","DOI":"10.1109\/TNNLS.2025.3561225","article-title":"A Survey and Evaluation of Adversarial Attacks in Object Detection","volume":"36","author":"Nguyen","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Lyssenko, M., Gladisch, C., Heinzemann, C., Woehrle, M., and Triebel, R. (2021). From Evaluation to Verification: Towards Task-oriented Relevance Metrics for Pedestrian Detection in Safety-critical Domains. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE.","DOI":"10.1109\/CVPRW53098.2021.00013"},{"key":"ref_148","unstructured":"Ujjwal, U. (2019). Handling the Speed-Accuracy Trade-Off in Deep-Learning Based Pedestrian Detection Systems, HAL (Le Centre pour la Communication Scientifique Directe)."},{"key":"ref_149","unstructured":"Dong, X., and Cappuccio, M.L. (2023). Why Autonomous Vehicles Are Not Ready Yet: A Multi-Disciplinary Review of Problems, Attempted Solutions, and Future Directions. arXiv."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.1109\/TNSE.2021.3139335","article-title":"Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection","volume":"9","author":"Zhuang","year":"2021","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_151","first-page":"134","article-title":"GPT-4V Takes the Wheel: Promises and Challenges for Pedestrian Behavior Prediction","volume":"3","author":"Huang","year":"2024","journal-title":"Proc. AAAI Symp. Ser."},{"key":"ref_152","unstructured":"Brown, P., Perez, J.S.B., Shan, M., and Worrall, S. (2025). Multi-Modal Camera-Based Detection of Vulnerable Road Users. arXiv."},{"key":"ref_153","unstructured":"Zhou, X., Menassa, C.C., and Kamat, V.R. (2024). Cognitive Internet of Vulnerable Road Users in Traffic: Predictive Neural Modulations of Road Crossing Intention. arXiv."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.1109\/TPAMI.2023.3333838","article-title":"Delving into the devils of bird\u2019s-eye-view perception: A review, evaluation and recipe","volume":"46","author":"Li","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Hu, Y., Yang, J., Chen, L., Li, K., Sima, C., Zhu, X., Chai, S., Du, S., Lin, T., and Wang, W. (2023). Planning-oriented autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR52729.2023.01712"},{"key":"ref_156","unstructured":"Standley, T., Zamir, A., Chen, D., Guibas, L., Malik, J., and Savarese, S. (2020, January 13\u201318). Which tasks should be learned together in multi-task learning?. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Yeong, D.J., Panduru, K., and Walsh, J. (2025). Exploring the unseen: A survey of multi-sensor fusion and the role of explainable ai (xai) in autonomous vehicles. Sensors, 25.","DOI":"10.20944\/preprints202501.1423.v1"},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Petsiuk, V., Jain, R., Manjunatha, V., Morariu, V.I., Mehra, A., Ordonez, V., and Saenko, K. (2021). Black-box explanation of object detectors via saliency maps. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR46437.2021.01128"},{"key":"ref_159","unstructured":"Melo, A.N., Salinas, C., and \u00c1ngel Sotelo, M. (2023). Experimental Insights Towards Explainable and Interpretable Pedestrian Crossing Prediction. arXiv."},{"key":"ref_160","unstructured":"Pathiraja, B., Liu, C., and Senanayake, R. (2024). Fairness in Autonomous Driving: Towards Understanding Confounding Factors in Object Detection under Challenging Weather. arXiv."},{"key":"ref_161","unstructured":"Kanu-Asiegbu, A.M. (2025). Enhancing Pedestrian Safety By Detecting Pedestrian Activities and Using Multi-Modal Sensors for Pedestrian Detection. [Ph.D. Thesis, University of Michigan]."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"3013","DOI":"10.1051\/e3sconf\/202448803013","article-title":"PED-AI: Pedestrian Detection for Autonomous Vehicles using YOLOv5","volume":"488","author":"Malbog","year":"2024","journal-title":"E3S Web Conf."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s40537-024-00890-0","article-title":"Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques","volume":"11","author":"Qu","year":"2024","journal-title":"J. Big Data"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/7\/317\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T13:41:05Z","timestamp":1783690865000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/7\/317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,10]]},"references-count":163,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["jimaging12070317"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12070317","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,10]]}}}