Reproducibility depends on reporting the data, procedures, parameters, exclusions, and uncertainty needed for an independent team to reconstruct the analysis. This structured evidence review evaluates "ACEsplat: Accelerated 3D Gaussian Scene Regression via RGB and Poses Only" 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 reproducibility and reporting standards, 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 identifies the artifacts, reporting fields, and independent checks needed for a complete computational reconstruction.
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- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Journal
- Global Questions: An Interdisciplinary Review
- Volume
- 1 (2026)
- Article number
- gq20260030
- License
- CC BY 4.0
