From FSD to FSC: Enabling Full Smart-Communication in Autonomous Vehicles Through Full Self-Driving Models

Abstract

In-vehicle wireless systems, such as vehicle-to-satellite communication, face challenges in adapting to rapidly changing environments due to the inherent latency of aligning high-precision beamforming with distant base stations (e.g., satellites). Motivated by the robust sensing capabilities of today’s autonomous vehicles, we propose modeling the radio environment in parallel. This allows the in-vehicle communication subsystem to predict channel conditions along the vehicle’s planned trajectory and precompute optimal beamforming parameters in advance, effectively addressing rapid environmental changes. To this end, we introduce Full Smart-Communication (FSC), a novel framework that repurposes intermediate feature representations—such as planned trajectories, occupancy maps, and posture estimates—from a vehicle’s end-to-end Full Self-Driving (FSD) model to derive spatio-temporal stochastic radio radiance fields. This approach avoids direct access to raw sensor streams while effectively correlating kinematic and environmental states with radio propagation dynamics. Our comprehensive experiments demonstrate that FSC achieves a mean gain of 15 dB and maintains less than one-degree beamforming error at direct upcoming positions. Additionally, FSC reduces power consumption by 70% and GPU memory usage by 94% through the hitchhiking approach. By leveraging FSD-derived environmental awareness for communication, our work bridges the gap between autonomous navigation and robust connectivity, unlocking the synergy of perception and radio resource management in next-generation self-driving vehicles.