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This report investigates the history and impact of Generative Models and Connected and Automated Vehicles (CAVs), two groundbreaking forces pushing progress in technology and transportation.
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Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., and Langs, G. (2017). Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In International conference on information processing in medical imaging (pp. 146-157). Springer, Cham
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Vaswani, Ashish, et al. ”Attention is all you need.” Advances in neural information processing systems 30 (2017)
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Gan, Chuang, et al. ”Stylenet: Generating attractive visual captions with styles.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2017
2017
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2018
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Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D.,and Meger, D. (2018). Deep reinforcement learning that matters. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1)
2018
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Gu, Jiuxiang, et al. ”Recent advances in convolutional neural networks.” Pattern recognition 77 (2018): 354-377.
2018
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Shum, Heung-Yeung, Xiao-dong He, and Di Li. ”From Eliza to XiaoIce: challenges and opportunities with social chatbots.” Frontiers of Information Technology & Electronic Engineering 19 (2018): 10-26
2018
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2018
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Ian Vázquez-Rowe, Ramzy Kahhat, Gustavo Larrea-Gallegos, Kurt Ziegler-Rodriguez, Peru’s road to climate action: Are we on the right path? The role of life cycle methods to improve Peruvian national contributions,Science of The Total Environment,Volume 659,2019,Pages 249-266,ISSN 0048-9697,https://doi.org/10.1016/j.scitotenv.2018.12.322
2018
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R. McCauley, “The 6 Challenges of Autonomous Vehicles and How to Overcome Them,” Govtech.com, 2019. https://www.govtech.com/fs/The-6-Challenges-of-Autonomous-Vehicles-and-How-to-Overcome-Them.html
2019
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C. Hao et al., ”NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving,” 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Westminster, CO, USA, 2019, pp. 1-8, doi: 10.1109/ICCAD45719.2019.8942055
2019
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Brock, A., Donahue, J., and Simonyan, K. (2019). Large scale GAN training for high fidelity natural image synthesis. In Proceedings of the International Conference on Learning Representations (ICLR)
2019
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Karras, T., Laine, S.,and Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4401-4410)
2019
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Chesney, R., and Citron, D. K. (2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107, 1753
2019
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Tan, M., Chen, B., Pang, R., Vasudevan, V., and Le, Q. V. (2019). Mnasnet: Platform-aware neural architecture search for mobile. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2820-2828)
2019
Cited alongside, same era.
Elsken, T., Metzen, J. H., and Hutter, F. (2019). Neural architecture search: A survey. Journal of Machine Learning Research, 20(55), 1-21
2019
Cited alongside, same era.
Arnelid, Henrik, Edvin Listo Zec, and Nasser Mohammadiha. ”Recurrent conditional generative adversarial networks for autonomous driving sensor modelling.” 2019 IEEE Intelligent transportation systems conference (ITSC). IEEE, 2019
2019
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Cunnington, Daniel, et al. ”A generative policy model for connected and autonomous vehicles.” 2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2019
2019
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Shin, Y., Kim, S., Jo, W., & Shon, T. (2022). Digital forensic case studies for in-vehicle infotainment systems using Android Auto and Apple CarPlay. Sensors, 22(19), 7196
2022
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McKinsey, “Autonomous driving’s future: Convenient and connected — McKinsey,” www.mckinsey.com, Jan. 06, 2023. https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/autonomous-drivings-future-convenient-and-connected
2023
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2023
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H. Lin, Y. Liu, S. Li and X. Qu, ”How Generative Adversarial Networks Promote the Development of Intelligent Transportation Systems: A Survey,” in IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 9, pp. 1781-1796, September 2023, doi: 10.1109/JAS.2023.123744
2023
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Radford, Alec, et al. ”Language models are unsupervised multitask learners.” OpenAI blog 1.8 (2019): 9
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Aradi, ”Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles,” in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 2, pp. 740-759, Feb. 2022, doi: 10.1109/TITS.2020.3024655
2020
Cited alongside, same era.
A. Alharin, T. -N. Doan and M. Sartipi, ”Reinforcement Learning Interpretation Methods: A Survey,” in IEEE Access, vol. 8, pp. 171058-171077, 2020, doi: 10.1109/ACCESS.2020.3023394
2020
Cited alongside, same era.
G. Balazs and W. Stechele, ”Neural Architecture Search for Automotive Grid Fusion Networks Under Embedded Hardware Constraints,” 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA, 2020, pp. 79-86, doi: 10.1109/ICMLA51294.2020.00022
2020
Cited alongside, same era.
Sharma, V., You, I., Kumar, R., Zeadally, S., and Qiu, M. (2020). Autonomous vehicles: Security, safety, and privacy issues. IEEE Access, 8, 193893-193902
2020
Cited alongside, same era.
Smith, J. A., & Johnson, D. B. (2020). ”Enhancing CAVs Communication with 5G and DSRC Integration.” Journal of Transport and Communication Innovation, 18(2), 34-49
2020
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Floridi, Luciano, and Massimo Chiriatti. ”GPT-3: Its nature, scope, limits, and consequences.” Minds and Machines 30 (2020): 681-694
2020
Cited alongside, same era.
Z. Xiao, J. Shu, H. Jiang, G. Min, J. Liang and A. Iyengar, ”Toward Collaborative Occlusion-free Perception in Connected Autonomous Vehicles,” in IEEE Transactions on Mobile Computing, doi: 10.1109/TMC.2023.3298643
2023
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2023
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2023
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M. Ambrogi, “10 Ways to Improve the Performance of Retrieval Augmented Generation Systems,” Medium, Sep. 18, 2023. https://towardsdatascience.com/10-ways-to-improve-the-performance-of-retrieval-augmented-generation-systems-5fa2cee7cd5c
2023
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Mokrane, Adel. Autonomous navigation of a rotary wing flying vehicles for precision agriculture. Diss. Université Paris-Saclay; Université Abou Bekr Belkaid (Tlemcen, Algérie), 2023
2023
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Epstein, Ziv, et al. ”Art and the science of generative AI.” Science 380.6650 (2023): 1110-1111
2023
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Zhou, Nabus. The Ethical Implications of DALL-E: Opportunities and Challenges. 2023. The Ethical Implications of DALL-E: Opportunities and Challenges
2023
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GPT-4 Technical Report. arXiv:2303.08774. 2023. GPT-4 Technical Report
2023
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Lucchi N. ChatGPT: A Case Study on Copyright Challenges for Generative Artificial Intelligence Systems. European Journal of Risk Regulation. Published online 2023:1-23. doi:10.1017/err.2023.59
2023
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Siriwardhana S, Weerasekera R, Wen E, et al. Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering. Trans Assoc Comput Linguist. 2023;11:1-17. Published 2023. doi:10.1162/tacla00530
2023
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Parkinson, G. M., Mazri, A., & Li, G. (2023). Exploration of issues, challenges and latest developments in autonomous cars. Journal of Big Data
2023
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McKinsey & Company. (2023). The future of autonomous vehicles (AV). Retrieved from https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/the-future-of-autonomous-vehicles
2023
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Gandhi, G. M., et al. (2023). Exploration of issues, challenges, and latest developments in autonomous cars. Journal of Big Data. https://journalofbigdata.springeropen.com/articles/10.1186/s40537-023-00628-2
2023
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Liu, H., Li, C., Wu, Q., & Lee, Y. J. (2023). Visual instruction tuning. Advances in neural information processing systems, 36, 34892-34916
2023
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2023
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2024
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2024
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Julio C. S. Dos Anjos, Kassiano J. Matteussi, Fernanda C. Orlandi, Jorge L. V. Barbosa, Jorge Sá Silva, Luiz F. Bittencourt, and Cláudio F. R. Geyer. 2023. A Survey on Collaborative Learning for Intelligent Autonomous Systems. ACM Comput. Surv. 56, 4, Article 98 (April 2024), 37 pages. https://doi.org/10.1145/3625544
2024
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Guarnera, Luca, Oliver Giudice, and Sebastiano Battiato. ”Mastering Deepfake Detection: A Cutting-Edge Approach to Distinguish GAN and Diffusion-Model Images.” ACM Transactions on Multimedia Computing, Communications and Applications (2024)
2024
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The Business Research Company. Vehicle-to-Vehicle (V2V) Communication Global Market Report 2024
2024
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Moon, S., Madotto, A., Lin, Z., Nagarajan, T., Smith, M., Jain, S., … & Kumar, A. (2024, November). Anymal: An efficient and scalable any-modality augmented language model. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track (pp. 1314-1332)
2024
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Wu, S., Fei, H., Qu, L., Ji, W., & Chua, T. S. (2024, July). Next-gpt: Any-to-any multimodal llm. In Forty-first International Conference on Machine Learning
2024
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Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., … & Wen, J. (2024). A survey on large language model based autonomous agents. Frontiers of Computer Science, 18(6), 186345
2024
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Xu, Z., Zhang, Y., Xie, E., Zhao, Z., Guo, Y., Wong, K. Y. K., … & Zhao, H. (2024). Drivegpt4: Interpretable end-to-end autonomous driving via large language model. IEEE Robotics and Automation Letters
2024
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Bruce, J., Dennis, M. D., Edwards, A., Parker-Holder, J., Shi, Y., Hughes, E., … & Rocktäschel, T. (2024, January). Genie: Generative interactive environments. In Forty-first International Conference on Machine Learning
2024
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2025
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2025
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2025
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Li, X. (2025, January). A Review of Prominent Paradigms for LLM-Based Agents: Tool Use, Planning (Including RAG), and Feedback Learning. In Proceedings of the 31st International Conference on Computational Linguistics (pp. 9760-9779)
2025
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