5G Advanced and 6G V2X Environments require intelligence that is real-time, resource-light and robust across high-density vehicle mobility, non-stationary channels and hard ware constrained edge nodes. Existing AI-driven resource man agement approaches — including CNNs, LSTMs, GNNs, Deep Reinforcement Learning, Transformers, and Federated Learning — have limitations, such as high computational overhead, large data requirements, poor generalization under topology churn, and catastrophic forgetting under continuous retraining. These limitations render them ill-suited for deployment, especially at the level of radio units. To address this issue, this paper proposes NeuroChaos-V2X, a chaos-inspired edge intelligence framework that integrates vehicle mobility, wireless channel, and edge computing states into a unified multi-domain state vector. A Neu roChaos Feature Transformation module, employing GLS and Logistic-map neurons, converts raw network measurements into chaos-derived descriptors — firing time, firing rate, energy, and entropy — without requiring deep architectures or large training datasets. These features drive a unified NeuroChaos Decision Engine supporting edge server selection, task offloading, resource prioritization, and handover management simultaneously. The framework’s low-sample learning capability, CPU-only footprint, and sensitivity to small state perturbations make it inherently suited to resource-constrained V2X edge tiers. This position paper establishes the conceptual foundation and motivates experimental validation against GNN, DRL, and Transformer baselines in future 6G-V2X simulation environments.
