DinoLink - A Token-Centric Representation Compression Framework for Bandwidth-Constrained Collaborative V2X Perception: Resource Efficiency and Performance Trade-offs

Efficiency claims should state which resources are saved, what performance is exchanged, and whether the trade-off remains acceptable at operational scale. This structured evidence review evaluates "DinoLink: A Token-Centric Representation Compression Framework for Bandwidth-Constrained Collaborative V2X Perception" alongside nine author-disjoint, topically matched publications in resource-efficient autonomous perception. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through resource efficiency and performance trade-offs, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda reports compute, memory, energy, latency, and maintenance costs beside task performance at realistic scale.

1. Zhu, T., Que, H., Yao, H., Xu, H., & Bao, Z. (2026). DinoLink: A Token-Centric Representation Compression Framework for Bandwidth-Constrained Collaborative V2X Perception. arXiv. https://doi.org/10.48550/arXiv.2606.26398 DOI
2. Costa, B.-T., Pereira, C., & Maykol Pinto, A. (2025). PerceptNet-V2X: Perception Network for Vehicle to Everything Scenarios in Autonomous Driving. IEEE Access, 13, 182645-182660. https://doi.org/10.1109/access.2025.3624285 DOI
3. Chandrashekhar, B., & Shakkeera, L. (2025). Reinforcement learning in Autonomous vehicle communication under V2X Framework using edge computing and Privacy-Preserving AI. 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS), 1-8. https://doi.org/10.1109/iccams65118.2025.11234562 DOI
4. Guo, A., Zhang, S., Tang, E., Gao, X., Pang, H., Tian, H., Mu, Y., Wen, W., Fang, C., & Chen, Z. (2025). When Autonomous Vehicle Meets V2X Cooperative Perception: How Far Are We?. 2025 40th IEEE/ACM International Conference on Automated Software Engineering (ASE), 1169-1181. https://doi.org/10.1109/ase63991.2025.00101 DOI
5. Asaju, B.-J., Jung, W., & Wakili, A. (2025). A Survey of Multi-Layered Cybersecurity Threats in Connected and Autonomous Vehicles: Risks from Edge Computing and V2x Protocols. . https://doi.org/10.2139/ssrn.5260375 DOI
6. Garcia, C.-E., Camana, M.-R., Mohammad, A.-B., Querol, J., & Chatzinotas, S. (2024). Edge Learning Optimization in Task-Oriented NOMA Communications for Autonomous Vehicle Perception. 2024 IEEE Wireless Communications and Networking Conference (WCNC), 1-6. https://doi.org/10.1109/wcnc57260.2024.10570514 DOI
7. Wang, S. (2025). Edge Intelligence for V2X Communications: Advances in Collaborative Perception and Privacy Preservation. Journal of Big Data and Computing, 3(4), 73-81. https://doi.org/10.62517/jbdc.202501409 DOI
8. Richards, E., Thapa, B., & Mashayekhy, L. (2025). Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception. 2025 IEEE International Conference on Edge Computing and Communications (EDGE), 13-22. https://doi.org/10.1109/edge67623.2025.00011 DOI
9. Roy, A. (2025). Beyond V2X: A Review of Large Language Models in In-Vehicle Autonomous Driving Systems. . https://doi.org/10.36227/techrxiv.175571972.28517288/v1 DOI
10. Soorchaei, B.-E., Raftari, A., & Fallah, Y.-P. (2025). Extensible Heterogeneous Collaborative Perception in Autonomous Vehicles with Codebook Compression. Robotics, 14(12), 186. https://doi.org/10.3390/robotics14120186 DOI