aiqbench analysis
This research introduces RSTA, a framework designed to enhance Vehicle-to-Everything (V2X) collaborative perception by addressing coupled degradations from domain shifts and wireless channel corruption simultaneously. By employing reliability-gated entropy minimization and cross-domain prototype alignment, the system achieves significant performance gains with minimal parameter updates during deployment. The work represents a notable advancement in making semantic communication robust for real-world autonomous driving scenarios under varying environmental conditions.
Domain-Generalized Adaptive Semantic Communication for Collaborative Perception
arXiv:2608.00056v1 Announce Type: cross Abstract: We propose RSTA, a domain-generalized semantic communication framework enabling source-free V2X collaborative perception under both observation-domain shift and unseen wireless channel conditions. In V2X, received semantic tokens suffer coupled degradation from pre-transmission domain drift and in-transit channel corruption; existing methods address only one source, leaving adaptation misled by tokens that are simultaneously off-domain and physically degraded. RSTA trains a pre-deployment semantic encoder for transmission stability via cross-domain prototype alignment and cross-channel gradient consistency, and updates a lightweight in-deployment decoder adapter through reliability-gated entropy minimization that restricts gradients to tokens ranked high in both semantic relevance and channel fidelity. A theoretical task robustness decomposition links each loss term to a distinct degradation source, grounding each algorithmic component in a measurable error mode. Trained on AWGN and tested on unseen Rayleigh fading, RSTA achieves +7.2 AP@0.7 over pre-deployment domain generalization on cross-weather tasks and +5.5 on cross-dataset tasks across four V2X benchmarks, updating only 0.21\% of parameters in-deployment with zero inter-agent synchronization overhead.
Abstract:We propose RSTA, a domain-generalized semantic communication framework enabling source-free V2X collaborative perception under both observation-domain shift and unseen wireless channel conditions. In V2X, received semantic tokens suffer coupled degradation from pre-transmission domain drift and in-transit channel corruption; existing methods address only one source, leaving adaptation misled by tokens that are simultaneously off-domain and physically degraded. RSTA trains a pre-deployment semantic encoder for transmission stability via cross-domain prototype alignment and cross-channel gradient consistency, and updates a lightweight in-deployment decoder adapter through reliability-gated entropy minimization that restricts gradients to tokens ranked high in both semantic relevance and channel fidelity. A theoretical task robustness decomposition links each loss term to a distinct degradation source, grounding each algorithmic component in a measurable error mode. Trained on AWGN and tested on unseen Rayleigh fading, RSTA achieves +7.2 AP@0.7 over pre-deployment domain generalization on cross-weather tasks and +5.5 on cross-dataset tasks across four V2X benchmarks, updating only 0.21\% of parameters in-deployment with zero inter-agent synchronization overhead.
Accepted by IEEE ICCC 2026. 6 pages, 5 figures
Signal Processing (eess.SP); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
arXiv:2608.00056 [eess.SP]
(or arXiv:2608.00056v1 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2608.00056 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 270 words
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