Applying Global and Local Attention to V2X Systems

The distinction between local and global attention is especially relevant in Vehicle-to-Everything (V2X) communication systems, where vehicles must process both nearby and long-range information under strict latency and reliability constraints.

Local Attention in V2X

In a V2X setting, sliding-window (local) attention can be used to model immediate surroundings, such as:

  • Nearby vehicles (V2V)
  • Adjacent lanes and intersections
  • Short-range sensor or roadside unit (RSU) messages

Using Qs, Ks, Vs, the model efficiently focuses on spatially or temporally adjacent entities. This is critical for real-time tasks such as collision avoidance, lane changing, and cooperative perception, where local context dominates decision-making.

Global Attention in V2X

Global attention, computed using Qg, Kg, Vg, enables the model to capture long-range dependencies that are also vital in intelligent transportation systems. Examples include:

  • Traffic congestion patterns several kilometers ahead
  • Global routing or navigation updates
  • Emergency vehicle broadcasts
  • Infrastructure-to-vehicle (I2V) coordination signals

By allowing selected tokens—such as infrastructure messages or priority vehicles—to attend globally, the model can integrate strategic, system-wide information without incurring the full computational cost of dense attention.

Why Separate Projections Are Important for V2X

V2X data is inherently heterogeneous: local interactions demand fine-grained, high-frequency modeling, while global signals often encode slower, high-level context. Using separate linear projections for local and global attention allows the model to learn representations that are specialized for these distinct roles.

This design mirrors the approach introduced in the Longformer architecture, where separate projections improve modeling capacity while maintaining scalability for long sequences.

Initialization and Stability

Initializing the global attention projections with the same values as the local projections provides a stable starting point for training. In V2X applications, this helps ensure reliable convergence when learning from noisy, asynchronous, and distributed communication signals.

Implications for Intelligent Transportation Systems

By combining sliding-window and global attention with separate linear projections, Transformer-based models can efficiently scale to large V2X environments while preserving both real-time responsiveness and global situational awareness. This makes the approach well-suited for advanced applications such as cooperative driving, traffic prediction, and autonomous fleet coordination.

Reference

This discussion is informed by the design principles introduced in:

Beltagy, I., Peters, M. E., & Cohan, A. (2020). Longformer: The Long-Document Transformer. Allen Institute for Artificial Intelligence.

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