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Cloud-Edge-Terminal Collaborative AIGC for Autonomous Driving (2407.01956v1)
Published 2 Jul 2024 in eess.SY, cs.RO, and cs.SY
Abstract: In dynamic autonomous driving environment, Artificial Intelligence-Generated Content (AIGC) technology can supplement vehicle perception and decision making by leveraging models' generative and predictive capabilities, and has the potential to enhance motion planning, trajectory prediction and traffic simulation. This article proposes a cloud-edge-terminal collaborative architecture to support AIGC for autonomous driving. By delving into the unique properties of AIGC services, this article initiates the attempts to construct mutually supportive AIGC and network systems for autonomous driving, including communication, storage and computation resource allocation schemes to support AIGC services, and leveraging AIGC to assist system design and resource management.
- T. Brown et al., “Language models are few-shot learners,” Advances in neural information processing systems, vol. 33, pp. 1877–1901, 2020.
- R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 10 684–10 695.
- M. Cherti, R. Beaumont, R. Wightman, M. Wortsman, G. Ilharco, C. Gordon, C. Schuhmann, L. Schmidt, and J. Jitsev, “Reproducible scaling laws for contrastive language-image learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 2818–2829.
- Z. Xu, Y. Zhang, E. Xie, Z. Zhao, Y. Guo, K. K. Wong, Z. Li, and H. Zhao, “DriveGPT4: Interpretable end-to-end autonomous driving via large language model,” arXiv preprint arXiv:2310.01412, 2023.
- A. Seff, B. Cera, D. Chen, M. Ng, A. Zhou, N. Nayakanti, K. S. Refaat, R. Al-Rfou, and B. Sapp, “Motionlm: Multi-agent motion forecasting as language modeling,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (CVPR), 2023, pp. 8579–8590.
- J. Mao, Y. Qian, H. Zhao, and Y. Wang, “GPT-driver: Learning to drive with GPT,” arXiv preprint arXiv:2310.01415, 2023.
- R. Zhang, K. Xiong, H. Du, D. Niyato, J. Kang, X. Shen, and H. V. Poor, “Generative AI-enabled vehicular networks: Fundamentals, framework, and case study,” arXiv preprint arXiv:2304.11098, 2023.
- M. Xu, D. Niyato, J. Chen, H. Zhang, J. Kang, Z. Xiong, S. Mao, and Z. Han, “Generative AI-empowered simulation for autonomous driving in vehicular mixed reality metaverses,” arXiv preprint arXiv:2302.08418, 2023.
- M. Li, J. Lin, Y. Ding, Z. Liu, J.-Y. Zhu, and S. Han, “GAN compression: Efficient architectures for interactive conditional GANs,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 5284–5294.
- H. Jiang, Q. Wu, C.-Y. Lin, Y. Yang, and L. Qiu, “Llmlingua: Compressing prompts for accelerated inference of large language models,” arXiv preprint arXiv:2310.05736, 2023.
- X. Cheng, H. Zhang, J. Zhang, S. Gao, S. Li, Z. Huang, L. Bai, Z. Yang, X. Zheng, and L. Yang, “Intelligent multi-modal sensing-communication integration: Synesthesia of machines,” IEEE Communications Surveys and Tutorials, 2023.
- M. Xu, D. Niyato, H. Zhang, J. Kang, Z. Xiong, S. Mao, and Z. Han, “Joint foundation model caching and inference of generative AI services for edge intelligence,” arXiv preprint arXiv:2305.12130, 2023.
- M. Xu et al., “Unleashing the power of edge-cloud generative AI in mobile networks: A survey of AIGC services,” arXiv preprint arXiv:2303.16129, 2023.
- K. W. Tindell, A. Burns, and A. J. Wellings, “Allocating hard real-time tasks: an NP-hard problem made easy,” Real-Time Systems, vol. 4, no. 2, pp. 145–165, 1992.
- S. Iqbal and F. Sha, “Actor-attention-critic for multi-agent reinforcement learning,” in International Conference on Machine Learning (ICML). PMLR, 2019, pp. 2961–2970.