Semantic anomaly detection with large language models

Language Models are Few-Shot Learners

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works

End-to-End Object Detection with Transformers

Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285 · see citing works

Advances in Neural Information Processing Systems 29

Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works

Advances in Neural Information Processing Systems 14

(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 9,184 · see citing works

Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471 · see citing works

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263 · see citing works

CARLA: An open urban driving simulator

Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718 · see citing works

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2016) · arXiv · doi:10.48550/arxiv.1612.01474 · Cited by 3,666 · see citing works

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 2,720 · cited 1× · see citing works

BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., & Darrell, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00271 · Cited by 2,641 · see citing works

Unbiased look at dataset bias

Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440 · see citing works

Shortcut learning in deep neural networks

Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., & Wichmann, F. A. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00257-z · Cited by 2,224 · see citing works

Large language models are zero-shot reasoners

Kojima, T., Gu, S. (., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1613 · Cited by 2,013 · see citing works

A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Hendrycks, D., & Gimpel, K. (2016) · arXiv · doi:10.48550/arxiv.1610.02136 · Cited by 1,968 · see citing works

Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Li, J., Li, D., Xiong, C., & Hoi, S. (2022) · arXiv · doi:10.48550/arxiv.2201.12086 · Cited by 1,741 · see citing works

Deep One-Class Classification

Ruff, L., Vandermeulen, R. A., Görnitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Müller, E., & Kloft, M. (2018) · International Conference on Machine Learning · Cited by 1,719 · see citing works

A Continual Learning Survey: Defying Forgetting in Classification Tasks

Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., & Tuytelaars, T. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3057446 · Cited by 1,647 · see citing works

Flamingo: a Visual Language Model for Few-Shot Learning

Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.14198 · Cited by 1,555 · see citing works

A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial AttacksPreprint

Lee, K., Lee, K., Lee, H., & Shin, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.03888 · Cited by 1,166 · see citing works

Enhancing The Reliability of Out-of-distribution Image Detection in Neural NetworksPreprint

Liang, S., Li, Y., & Srikant, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02690 · Cited by 1,117 · see citing works

A Unifying Review of Deep and Shallow Anomaly Detection

Ruff, L., Kauffmann, J., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3052449 · Cited by 1,080 · see citing works

Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B., & Snoek, J. (2019) · arXiv · doi:10.48550/arxiv.1906.02530 · Cited by 989 · see citing works

A survey of unsupervised deep domain adaptation

Wilson, G., & Cook, D. J. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3400066 · Cited by 987 · see citing works

Code as Policies: Language Model Programs for Embodied Control

Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P., & Zeng, A. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10160591 · Cited by 767 · see citing works

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Fu, C., Gopalakrishnan, K., Hausman, K., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.01691 · Cited by 624 · see citing works

PaLM-E: An Embodied Multimodal Language Model

Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.03378 · Cited by 591 · see citing works

In Search of Lost Domain Generalization

Gulrajani, I., & Lopez-Paz, D. (2020) · arXiv · doi:10.48550/arxiv.2007.01434 · Cited by 440 · see citing works

Kimera: From SLAM to spatial perception with 3D dynamic scene graphs

Rosinol, A., Violette, A., Abate, M., Hughes, N., Chang, Y., Shi, J., Gupta, A., & Carlone, L. (2021) · Int. J. Robotics Res. · doi:10.1177/02783649211056674 · Cited by 418 · see citing works

C2AE: Class Conditioned Auto-Encoder for Open-Set Recognition

Oza, P., & Patel, V. M. (2019) · 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.2019.00241 · Cited by 407 · see citing works

A novelty detection approach to classification

Japkowicz, N., Myers, C. E., & Gluck, M. A. (1995) · Cited by 392 · see citing works

Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items

Downs, L., Francis, A. G., Jr., Koenig, N., Kinman, B., Hickman, R., Reymann, K., McHugh, T. B., & Vanhoucke, V. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811809 · Cited by 382 · see citing works

Simple Open-Vocabulary Object Detection

Minderer, M., Gritsenko, A. A., Stone, A., Neumann, M., Weissenborn, D., Dosovitskiy, A., Mahendran, A., Arnab, A., Dehghani, M., Shen, Z., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20080-9_42 · Cited by 365 · see citing works

Deep Evidential RegressionPreprint

Amini, A., Schwarting, W., Soleimany, A. P., & Rus, D. L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.02600 · Cited by 290 · see citing works

Inner Monologue: Embodied Reasoning through Planning with Language Models

Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., et al. (2022) · arXiv · doi:10.48550/arxiv.2207.05608 · Cited by 287 · see citing works

Simple and principled uncertainty estimation with deterministic deep learning via distance awareness

Liu, J. Z., Lin, Z., Padhy, S., Tran, D., Bedrax-Weiss, T., & Lakshminarayanan, B. (2020) · arXiv · doi:10.48550/arxiv.2006.10108 · Cited by 237 · see citing works

Text2Motion: from natural language instructions to feasible plans

Lin, K., Agia, C., Migimatsu, T., Pavone, M., & Bohg, J. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10131-7 · Cited by 233 · see citing works

CLIPort: What and Where Pathways for Robotic Manipulation

Shridhar, M., Manuelli, L., & Fox, D. (2021) · arXiv · doi:10.48550/arxiv.2109.12098 · Cited by 223 · see citing works

Transporter Networks: Rearranging the Visual World for Robotic Manipulation

Zeng, A., Florence, P., Tompson, J., Welker, S., Chien, J., Attarian, M., Armstrong, T., Krasin, I., Duong, D., Wahid, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.14406 · Cited by 200 · see citing works

Untitled work

(2018) · UCL Discovery (University College London) · Cited by 183 · see citing works

LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action

Shah, D., Osinski, B., Ichter, B., & Levine, S. (2022) · arXiv · doi:10.48550/arxiv.2207.04429 · Cited by 179 · see citing works

Causal Confusion in Imitation LearningPreprint

de Haan, P., Jayaraman, D., & Levine, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.11979 · Cited by 161 · see citing works

Safe Visual Navigation via Deep Learning and Novelty Detection

Richter, C., & Roy, N. (2017) · Robotics: Science and Systems · doi:10.15607/rss.2017.xiii.064 · Cited by 160 · see citing works

Language Models of Code are Few-Shot Commonsense Learners

Madaan, A., Zhou, S., Alon, U., Yang, Y., & Neubig, G. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.90 · Cited by 131 · see citing works

A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges

Salehi, M., Mirzaei, H., Hendrycks, D., Li, Y., Rohban, M. H., & Sabokrou, M. (2021) · arXiv · doi:10.48550/arxiv.2110.14051 · Cited by 118 · see citing works

MOOD: Multi-level Out-of-distribution Detection

Lin, Z., Roy, S. D., & Li, Y. (2021) · 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/cvpr46437.2021.01506 · Cited by 86 · see citing works

Towards Robust CNN-based Object Detection through Augmentation with Synthetic Rain Variations

Volk, G., Müller, S., von Bernuth, A., Hospach, D., & Bringmann, O. (2019) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2019.8917269 · Cited by 85 · see citing works

Introspective perception: Learning to predict failures in vision systems

Daftry, S., Zeng, S., Bagnell, J. A., & Hebert, M. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759279 · Cited by 75 · see citing works

Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty

McAllister, R., Kahn, G., Clune, J., & Levine, S. (2018) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2019.8793552 · Cited by 41 · see citing works

Epistemic Neural NetworksPreprint

Osband, I., Wen, Z., Asghari, S. M., Dwaracherla, V., Ibrahimi, M., Lu, X., & Roy, B. V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.08924 · Cited by 40 · see citing works

Monitoring and Diagnosability of Perception Systems

Antonante, P., Spivak, D. I., & Carlone, L. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636497 · Cited by 37 · see citing works

IVOA: Introspective Vision for Obstacle Avoidance

Rabiee, S., & Biswas, J. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968176 · Cited by 29 · see citing works

Detect, Reject, Correct: Crossmodal Compensation of Corrupted Sensors

Lee, M. A., Tan, M., Zhu, Y., & Bohg, J. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561847 · Cited by 24 · see citing works

Sketching Curvature for Efficient Out-of-Distribution Detection for Deep Neural NetworksPreprint

Sharma, A., Azizan, N., & Pavone, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.12567 · Cited by 13 · see citing works

Three-dimensional reconstruction using SFM for actual pedestrian classification

Gomez‐Donoso, F., Castaño-Amorós, J., Escalona, F., & Cazorla, M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119006 · Cited by 12 · see citing works

Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications

Banerjee, S., Sharma, A., Schmerling, E., Spolaor, M., Nemerouf, M., & Pavone, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25056-9_9 · Cited by 1 · see citing works

A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly

Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., & Zhang, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100211 · Cited by 1,083 · see citing works

A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

(2024) · doi:10.1109/access.2024.3365742 · Cited by 934 · see citing works

DriveLM: Driving with Graph Visual Question Answering

Sima, C., Renz, K., Chitta, K., Chen, L., Zhang, H., Xie, C., Beißwenger, J., Luo, P., Geiger, A., & Li, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72943-0_15 · Cited by 280 · see citing works

GeoGPT: An assistant for understanding and processing geospatial tasks

Zhang, Y., Cheng, W., He, Z., & Yu, W. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.103976 · Cited by 143 · see citing works

Harnessing Large Language Models for Training-Free Video Anomaly Detection

Zanella, L., Menapace, W., Mancini, M., Wang, Y., & Ricci, E. (2024) · 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/cvpr52733.2024.01753 · Cited by 103 · see citing works

LLMScenario: Large Language Model Driven Scenario Generation

Chang, C., Wang, S., Zhang, J., Ge, J., & Li, L. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3392930 · Cited by 88 · see citing works

MapGPT: an autonomous framework for mapping by integrating large language model and cartographic tools

Zhang, Y., He, Z., Li, J., Lin, J., Guan, Q., & Yu, W. (2024) · Cartography and Geographic Information Science · doi:10.1080/15230406.2024.2404868 · Cited by 83 · see citing works

Large models for intelligent transportation systems and autonomous vehicles: A survey

Gan, L., Chu, W., Li, G., Tang, X., & Li, K. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102786 · Cited by 79 · see citing works

A Survey of Robot Intelligence with Large Language Models

Jeong, H., Lee, H., Kim, C., & Shin, S. (2024) · Applied Sciences · doi:10.3390/app14198868 · Cited by 80 · see citing works

Evaluating large language models for health-related text classification tasks with public social media data

Guo, Y., Ovadje, A., Al-Garadi, M. A., & Sarker, A. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae210 · Cited by 64 · see citing works

ChatCNC: Conversational machine monitoring via large language model and real-time data retrieval augmented generation

Jeon, J., Sim, Y., Lee, H., Han, C., Yun, D., Kim, E., Nagendra, S. L., Jun, M. B., Kim, Y. ‐., Lee, J., et al. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.01.018 · Cited by 64 · see citing works

Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Models

Yang, Y., Lee, K., Dariush, B., Cao, Y., & Lo, S.-Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73004-7_18 · Cited by 58 · see citing works

Anomaly diagnosis of connected autonomous vehicles: A survey

Fang, Y., Min, H., Wu, X., Wang, W., Zhao, X., Martínez‐Pastor, B., & Teixeira, R. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102223 · Cited by 45 · see citing works

Large language models for cyber resilience: A comprehensive review, challenges, and future perspectives

Ding, W., Abdel‐Basset, M., Ali, A. M., & Moustafa, N. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112663 · Cited by 41 · see citing works

Driving Everywhere with Large Language Model Policy Adaptation

Li, B., Wang, Y., Mao, J., Ivanovic, B., Veer, S., Leung, K., & Pavone, M. (2024) · 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/cvpr52733.2024.01416 · Cited by 41 · see citing works

Delving Into Multi-Modal Multi-Task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives

Luo, S. X., Chen, W., Tian, W., Liu, R., Hou, L., Zhang, X., Shen, H., Wu, R., Geng, S., Zhou, Y., et al. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3406372 · Cited by 35 · see citing works

Embodied Understanding of Driving Scenarios

Zhou, Y., Huang, L., Bu, Q., Zeng, J., Li, T., Qiu, H., Zhu, H., Guo, M., Qiao, Y., & Li, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73033-7_8 · Cited by 31 · see citing works

Large Language Models in Transportation: A Comprehensive Bibliometric Analysis of Emerging Trends, Challenges, and Future Research

Hassan, M., Kabir, M. E., Jusoh, M., An, H. K., Negnevitsky, M., & Li, C. (2025) · IEEE Access · doi:10.1109/access.2025.3589319 · Cited by 30 · see citing works

LLMs in Cyber Security: Bridging Practice and Education

Atlam, H. F. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9070184 · Cited by 27 · see citing works

DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving

Han, W., Guo, D., Xu, C.-Z., & Shen, J. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i3.32346 · Cited by 26 · see citing works

A Survey on Online Aggression: Content Detection and Behavioral Analysis on Social Media

Mane, S., Kundu, S., & Sharma, R. R. (2025) · ACM Computing Surveys · doi:10.1145/3711125 · Cited by 25 · see citing works

LLM-3D print: Large Language Models to monitor and control 3D printing

Jadhav, Y., Pak, P., & Farimani, A. B. (2025) · Additive manufacturing · doi:10.1016/j.addma.2025.105027 · Cited by 25 · see citing works

LLM-Based Operating Systems for Automated Vehicles: A New Perspective

Ge, J., Chang, C., Zhang, J., Li, L., Na, X., Lin, Y., Li, L., & Wang, F. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3399813 · Cited by 26 · see citing works

Prospective Role of Foundation Models in Advancing Autonomous Vehicles

Wu, J., Gao, B., Gao, J., Yu, J., Chu, H., Yu, Q., Gong, X., Chang, Y. M., Tseng, H. E., Chen, H., et al. (2024) · Research · doi:10.34133/research.0399 · Cited by 24 · see citing works

LLM-Driven Robots Risk Enacting Discrimination, Violence, and Unlawful Actions

Hundt, A., Azeem, R., Mansouri, M. I., & Brandão, M. (2025) · International Journal of Social Robotics · doi:10.1007/s12369-025-01301-x · Cited by 24 · see citing works

Coal Mine Accident Risk Analysis with Large Language Models and Bayesian Networks

Du, G., & Chen, A. (2025) · Sustainability · doi:10.3390/su17051896 · Cited by 23 · see citing works

Deep anomaly detection for time series: A survey

Jia, X., Peng, X., Peng, W., Zhao, B., Li, H., & Shen, C. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100787 · Cited by 20 · see citing works

Resilience Assessment of Large Language Models under Transient Hardware Faults

Agarwal, U. K., Chan, A., & Pattabiraman, K. (2023) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre59848.2023.00052 · Cited by 18 · see citing works

A Survey on Large Language Model-Powered Autonomous Driving

Zhu, Y., Wang, S., Zhong, W., Shen, N., Li, Y., Wang, S., Li, L., Wu, C., He, Z., & Li, L. (2025) · Engineering · doi:10.1016/j.eng.2025.07.038 · Cited by 20 · see citing works

ADAGENT: Anomaly Detection Agent With Multimodal Large Models in Adverse Environments

Zhang, M., Shen, Y., Yin, J., Lü, S., & Wang, X. (2024) · IEEE Access · doi:10.1109/access.2024.3480250 · Cited by 17 · see citing works

Generative AI Agents in Autonomous Machines: A Safety Perspective

Jabbour, J., & Reddi, V. J. (2024) · International Conference on Computer Aided Design · doi:10.1145/3676536.3698390 · Cited by 23 · see citing works

Empowering financial futures: Large language models in the modern financial landscape

Cao, X., Li, S., Katsikis, V. N., Khan, A. T., He, H., Liu, Z., Zhang, L., & Peng, C. (2024) · EAI Endorsed Transactions on AI and Robotics · doi:10.4108/airo.6117 · Cited by 17 · see citing works

Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving

Azarafza, M., Nayyeri, M., Steinmetz, C., Staab, S., & Rettberg, A. (2024) · International Conference on Control, Mechatronics and Automation (ICCMA) · doi:10.1109/iccma63715.2024.10843921 · Cited by 15 · see citing works

Automated Generation of Test Scenarios for Autonomous Driving Using LLMs

Danso, A. A., & Büker, U. (2025) · Electronics · doi:10.3390/electronics14163177 · Cited by 11 · see citing works

Edge-Enhanced Intelligence: A Comprehensive Survey of Large Language Models and Edge-Cloud Computing Synergy

Li, X., Li, H., Sun, C., Fan, Q., Han, Z., & Leung, V. C. M. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3587225 · Cited by 10 · see citing works

Towards practical artificial intelligence in Earth sciences

Sun, Z., ten Brink, T., Carande, W. H., Koren, G., Cristea, N. C., Jorgenson, C., Janga, B., Asamani, G. P., Achan, S., Mahoney, M., et al. (2024) · Computational Geosciences · doi:10.1007/s10596-024-10317-7 · Cited by 10 · see citing works

LIDAROC: Realistic LiDAR Cover Contamination Dataset for Enhancing Autonomous Vehicle Perception Reliability

Jati, G., Molan, M., Barchi, F., Bartolini, A., Mercurio, G., & Acquaviva, A. (2024) · IEEE Sensors Letters · doi:10.1109/lsens.2024.3434624 · Cited by 9 · see citing works

Generating Out-of-Distribution Scenarios Using Language Models

Aasi, E., Nguyen, P., Sreeram, S., Rosman, G., Karaman, S., & Rus, D. L. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127950 · Cited by 9 · see citing works

A Survey on the Application of Large Language Models in Scenario-Based Testing of Automated Driving Systems

Zhao, Y., Zhou, J., Bi, D., Mihalj, T., Hu, J., & Eichberger, A. (2026) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2026.3651004 · Cited by 9 · see citing works

Embodied Scene Understanding for Vision Language Models via MetaVQA

Wang, W., Duan, C., Peng, Z., Liu, Y., & Zhou, B. (2025) · 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/cvpr52734.2025.02091 · Cited by 8 · see citing works

End-to-End Autonomous Driving: From Classic Paradigm to Large Model Empowerment—A Comprehensive Survey

Dong, W., Lu, S., Chen, X., Zhang, S., Liu, Q., Liu, Z., Chen, L., Wang, H., & Cai, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3635092 · Cited by 7 · see citing works

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He, J., Yu, L., Li, C., Yang, R., Chen, F., Li, K., Zhang, M., Lei, S., Zhang, X., Beigi, M., et al. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.104057 · Cited by 7 · see citing works

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Singer, B., Lucas, K., Adiga, L., Jain, M., Bauer, L., & Sekar, V. (2026) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp63933.2026.00132 · Cited by 7 · see citing works

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Mahesh, T. R., Gangadevi, G., Mishra, K. K., Algarni, A. D., Saidani, O., & Kumar, A. (2025) · International Journal of Distributed Sensor Networks · doi:10.1155/dsn/6789771 · Cited by 6 · see citing works

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Foutter, M., Gammelli, D., Kruger, J., Foss, E., Bhoj, P., Guffanti, T., D’Amico, S., & Pavone, M. (2025) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero63441.2025.11068757 · Cited by 10 · see citing works

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Ren, J., Tang, T., Jia, H., Xu, Z., Fayek, H. M., Li, X., Ma, S., Xu, X., & Xia, F. (2025) · AI Magazine · doi:10.1002/aaai.70045 · Cited by 5 · see citing works

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Chanmas, G., Taveekitworachai, P., You, X., Thawonmas, R., Nukoolkit, C., & Dajpratham, P. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e35941 · Cited by 5 · see citing works

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Ilyas, S., Freeman, I., & Rottmann, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91672-4_13 · Cited by 5 · see citing works

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Zhao, R., Du, A., Cai, M., Zhu, Z., & He, B. (2026) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020090 · Cited by 4 · see citing works

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Liu, S., Shi, L., Shi, Y., Gao, Y., & Sun, X. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113210 · Cited by 4 · see citing works

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Zhang, Y., Zhao, Z., Zhang, D., & Dai, J. (2025) · International Conference on Power Electronics and Electric Drives (PEED) · doi:10.1109/peed63748.2025.00038 · Cited by 4 · see citing works

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Schoonbeek, T. J., Balachandran, G., Onvlee, H., Houben, T., Hung, S.-H., Kustra, J., de With, P. H. N., & van der Sommen, F. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3468157 · Cited by 4 · see citing works

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Asad, M., Ullah, I., Hafeez, M. A., Sistu, G., & Madden, M. G. (2025) · IEEE Access · doi:10.1109/access.2025.3577080 · Cited by 4 · see citing works

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Liu, Y., Wu, D., Ma, F., & Mao, W. (2025) · International Conference on Transportation Information and Safety · doi:10.1109/ictis68762.2025.11214874 · Cited by 3 · see citing works

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Ann, S., Kim, H. J., Park, S., Cho, S.-J., Kim, J., & Cho, H. (2026) · Electronics · doi:10.3390/electronics15020371 · Cited by 3 · see citing works

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Jeon, J., Sim, Y., Lee, H., Han, C., Yun, D., Kim, E., Nagendra, S. L., Jun, M. B. G., Kim, Y., Lee, S. W., et al. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.01.009 · Cited by 3 · see citing works

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Hu, C.-F., Wu, H., Wei, C., Chen, Q.-Q., Yue, H., Huang, W., Liu, C., Fu, T., & Wang, J. (2025) · Geocarto International · doi:10.1080/10106049.2025.2453618 · Cited by 3 · see citing works

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Zuzuárregui, M. A., & Carpin, S. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128633 · Cited by 5 · see citing works

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Zhang, J., Li, F., Wu, H., & Kumar, V. (2025) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon65708.2025.11235356 · Cited by 3 · see citing works

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Wang, T., Wu, D., Wang, J., Zhao, J., Wang, H., Xie, D., Zhang, H., & Hei, X. (2025) · Sensors · doi:10.3390/s25061849 · Cited by 2 · see citing works

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Roy, A., Cobb, A. D., Kaur, R., Jha, S. K., Bastian, N. D., Berenbeim, A., Thomson, R., Cruickshank, I. J., Velasquez, A., & Jha, S. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00890 · Cited by 2 · see citing works

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Pei, Y., Hao, M., Huang, Y., & Zou, Q. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.132836 · Cited by 2 · see citing works

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Saimbhi, S. S., & Akpinar, K. O. (2024) · International Conference on Artificial Intelligence, Metaverse and Cybersecurity (ICAMAC) · doi:10.1109/icamac62387.2024.10828788 · Cited by 2 · see citing works

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Landeta-López, P., García, J. M., Guevara‐Vega, C., & Ruiz–Cortés, A. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-7423-7_13 · Cited by 2 · see citing works

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Singhal, R., Singhal, I., Sharma, V., & Bansal, V. (2025) · Asset analytics · doi:10.1007/978-981-96-7556-2_25 · Cited by 2 · see citing works

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Toledo, F., Elbaum, S. G., Gopinath, D., Kaur, R., Mangal, R., Păsăreanu, C. S., Roy, A., & Jha, S. (2025) · IEEE Engineering Reliable Autonomous Systems (ERAS) · doi:10.1109/eras63351.2025.11135768 · Cited by 2 · see citing works

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NVIDIA, :, Azzolini, A., Bai, J., Brandon, H., Cao, J., Chattopadhyay, P., Chen, H., Chu, J., Cui, Y., et al. (2025) · arXiv · doi:10.48550/arxiv.2503.15558 · Cited by 1 · see citing works

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Wang, W., Li, C., Hu, L. N., Pang, B., Balducci, B., Marinova, D., Gordon, M., & Shang, Y. (2024) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri62200.2024.00042 · Cited by 1 · see citing works

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Qiao, Y., Li, L., Cheng, F., Zhang, J., Gao, J., & Zhu, H. (2025) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd64889.2025.11033478 · Cited by 2 · see citing works

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Diwanji, H., Liao, J.-Y., Tumu, A., Christensen, H. I., Vazquez-Chanlatte, M., & Tsuchiya, C. (2025) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv64158.2025.11097648 · Cited by 1 · see citing works

LLM-Driven Adaptive Prompt Optimization Framework for ADS-B Anomaly Detection

Li, S. L., Wang, B., Zhao, Z., Yang, Y. J., & Guan, Y.-J. (2025) · Aerospace · doi:10.3390/aerospace12100906 · Cited by 1 · see citing works

FUSCO: Distributed Agentic Data Acquisition and Fusion for Semantic Corner Case Detection

Luti, M., Harshbarger, I. A., Dopiriak, M., Pathanjaly, H., Gazda, J., & Levorato, M. (2025) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom64451.2025.11309348 · Cited by 1 · see citing works

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Zhao, Y., Ma, T., Wang, Z., Zhang, Z., Li, C., Liu, S., Cui, Z., Lv, M., Yu, H., & Peng, Z. (2026) · Journal of Advanced Transportation · doi:10.1155/atr/2814128 · Cited by 1 · see citing works

Foundation Models for Autonomous Driving Systems: An Initial Roadmap

Wu, X., Cheng, M., Ren, X., Hu, Q., Chen, J., Huang, Y., Cordy, M., Zhang, Y., Xie, X., Ma, L., et al. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3807455 · Cited by 3 · see citing works

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Gogineni, S., Yuvaraju, C., Suryanarayana, G., Returi, K. D., Mohan, V. M., & Rao, M. N. (2026) · IEEE International Conference on Emerging Computing and Intelligent Technologies (ICoECIT) · doi:10.1109/icoecit68303.2026.11498008 · Cited by 1 · see citing works

Generative Artificial Intelligence as Driver for Innovation in the Automotive Industry – A Systematic Analysis

Bischoff, L., & Stephan, M. (2025) · Springer eBooks · doi:10.1007/978-3-658-46485-1_6 · Cited by 1 · see citing works

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Azim, A., Clark, L. S., Lau, C. K. Y., Cobb, M., & Jenner, K. L. (2024) · Signal processing, sensor fusion, and target recognition. Conference · doi:10.1117/12.3013332

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Park, M., Nam, J., & Cho, H. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-03550-9_24

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Kohli, S., Dhir, S., & Kumud (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2474-6.ch008

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Hu, W. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11472-3

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Carter, J. M., Mancoridis, S., Protopapas, P., & Mitchell, B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-08067-7_9

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Divkoti, M. R. R., & Aguiar, A. P. (2025) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa65518.2025.11205775

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Xiang, W., Lei, Z., Wang, J., Huang, Y., Zheng, Q., Zhang, T., Zhao, A., & Sun, L. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3634580

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Jiang, K., Yan, F., Qiao, Y., Li, M., Li, Y., & Conti, M. (2025) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc65549.2025.11326227

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Chang, L., Xu, B., Zhang, X., Wang, J., Shi, T., Guo, R., & Du, Y. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.113997

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Chen, X., Wen, X., Bian, W., Wang, C., Chen, X., & Bao, J. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-17426-w

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Wu, J. P., Ye, Z., Zhao, X., Wang, W., & Shao, X. (2026) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi67283.2026.11412569

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Asai, K., Zhou, B., Huang, J., Arakawa, Y., & Mine, T. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401483

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Hubert, A., Elghazaly, G., & Frank, R. (2025) · Intelligent Autonomous Systems · doi:10.1109/icoias69065.2025.00012

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Loukas, P., Bassir, D., Chatzichristofis, S. A., & Amanatiadis, A. A. (2026) · Lecture notes in business information processing · doi:10.1007/978-3-032-18484-9_23

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Christensen, K. A., Tufte, A. G., Gusev, A. N., Sinha, R., Ganai, M., Alsos, O. A., Pavone, M., & Steinert, M. (2026) · Ocean Engineering · doi:10.1016/j.oceaneng.2026.124646

Foundation Model-Energized Anomaly Detection and Outlier Detection: A Survey

Xiao, F., & Fan, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-1468-6_5

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Zhang, T.-Y., Liu, J.-F., Guo, Y., Zhong, F.-Z., Bao, W., Dong, J., Liu, A.-S., Liu, X., & Cao, Y. (2026) · Chinese journal of electronics · doi:10.23919/cje.2025.00.095 · Cited by 2 · see citing works

A Survey of Methods for Enhancing the Robustness of Large Language Models

Li, D., & Li, D. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3435-6_29

Explainable Tabular Deep Learning for Network Anomaly Detection: A Comparative Study of AutoInt and DANet Architectures

Asal, B. (2026) · Cognitive Models and Artificial Intelligence Conference (AICCONF) · doi:10.1109/aicconf69182.2026.11600649

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Wen, Y., Fan, P., Mao, Z., Chi, F., Chenxuan, Z., & Lu, Y. (2026) · AI · doi:10.3390/ai7080299

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Bove, L., Epifani, L., Corallo, A., & Gervasi, M. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-23684-5_44

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Chen, J., Dang, Y., Chen, P., Liang, R., Gao, H., Gao, N., Huan, R., Zhao, D., Tian, X., & Shi-feng, Z. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3723187

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Fan, J., Rao, J., Wei, L., Li, G., Yuan, J., & Qi, Y. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.134549

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Stój, J., Gaj, P., Smołka, I., & KILJAN, A. (2026) · IEEE RO-MAN · doi:10.1109/ro-man72286.2026.11714914

Real-Time Anomaly Detection and Reactive Planning with Large Language Models

Sinha, R., Elhafsi, A., Agia, C., Foutter, M., Schmerling, E., & Pavone, M. (2024) · Robotics: Science and Systems · doi:10.15607/rss.2024.xx.114 · Cited by 52 · see citing works

Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured ReasoningPreprint

Brusnicki, R., Pop, D., Gao, Y., Piccinini, M., & Betz, J. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2510.18034

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Ronecker, M. P., Foutter, M., Elhafsi, A., Gammelli, D., Barakaiev, I., Pavone, M., & Watzenig, D. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2505.07998

Evaluation of Large Language Models for Anomaly Detection in Autonomous Vehicles

Loukas, P., Bassir, D., Chatzichristofis, S. A., & Amanatiadis, A. A. (2026) · Lecture notes in business information processing · doi:10.1007/978-3-032-18484-9_23

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Aydoğmuş, Ö., & Boztaş, G. (2026) · IEEE Access · doi:10.1109/access.2026.3658108 · Cited by 4 · see citing works