The high bandwidth demand of raw sensor data transmission remains a critical bottleneck for scalable Vehicle-to-Everything (V2X) networks. We propose Car2Vec - a self-supervised contrastive learning framework that generates compact latent embeddings from vehicle CAN bus data. Our initial experiments demonstrate the framework's ability to create visually distinguishable embeddings in latent space, successfully separating clusters for fuel from different suppliers and identifying varying levels of motor oil viscosity. These early results suggest the framework's potential to encode semantic state information including vehicle dynamics and driver context, and serve as efficient communication primitives for V2V/V2I applications, potentially reducing wireless overhead by orders of magnitude compared to raw telemetry. By moving computation to vehicle edge devices, Car2Vec aligns with the AI-RAN co-design vision, offering a promising direction for semantic-aware resource optimization in next-gen transportation networks.

Relay Protection and Automation (RPA) devices maintain power system stability by isolating faults using predefined or adaptive thresholds. However, even flexible configurations require manual pre-tuning, limiting their adaptability in dynamic grid environments, such as those with renewable energy integration. Artificial Intelligence (AI) augments RPA functionality by generalizing to new, unseen data, improving fault detection accuracy in transient or noisy scenarios. Our early studies demonstrate that embedded AI systems outperform static thresholds while operating in real time. These systems, implemented via neural networks on System-on-Chip (SoC) platforms with dedicated Neural Processing Units (NPUs), avoid cloud-dependent latency.