MotiMem - Motion-Aware Approximate Memory for Energy-Efficient Neural Perception in Autonomous Vehicles: Interface Design and Evidence Communication

Evidence must be communicated through interfaces that preserve uncertainty, support interpretation, and avoid turning a model output into an unexplained recommendation. This structured evidence review evaluates "MotiMem: Motion-Aware Approximate Memory for Energy-Efficient Neural Perception in Autonomous Vehicles" 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 interface design and evidence communication, 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 evaluates comprehension, uncertainty displays, actionable explanations, and the consequences of predictable interface misuse.

1. Que, H., Liu, M., Xie, J., Gao, H., Sun, J., Xu, H., Yao, H., & Qiao, F. (2026). MotiMem: Motion-Aware Approximate Memory for Energy-Efficient Neural Perception in Autonomous Vehicles. arXiv. https://doi.org/10.48550/arXiv.2603.27108 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