Jul 2026· International Conference on Computer Communications and Networks· pp. 1-6· 0 citations· 19 references
Abstract
Collective Perception (CP) in Vehicle-to-Everything (V2X) networks extends vehicle perception, but in high-density scenarios, simultaneous multi-sensor data exchange saturates the available 5.9 GHz channel bandwidth, degrading the Age of Information (AoI) and compromising safety. Existing approaches employ static transmission strategies that disregard both the semantic relevance of perceived data and real-time channel dynamics, leading to suboptimal resource utilization. CogniTensor addresses this gap by formulating adaptive multi-sensor transmission as a Markov Decision Process (MDP) solved via a Discrete Soft Actor-Critic (SAC-D) agent. It is the first framework to jointly optimize sensor selection, adaptive Tucker-based tensor compression, and transmission timing by reasoning over a composite state that includes perceptual uncertainty from Evidential Deep Learning, channel conditions, and vehicle dynamics. Evaluation demonstrates that CogniTensor achieves 58% lower bandwidth consumption and 9% higher resource efficiency (R/BW: 1.29 vs. 1.18) relative to static full-sensor transmission, while maintaining competitive AoI through intelligent, congestion-aware sensor selection.
Vehicle-to-Everything (V2X) cooperative perception improves 3-D detection by sharing intermediate features, but dense remote features may repeat context that the ego agent can infer locally. Most communication-efficient designs optimize masks or codes empirically, leaving a more basic question open: which remote eviden...
Cooperative perception can improve the situational awareness of connected and autonomous vehicles by exchanging complementary sensing information among nearby agents. However, systematic multi-vehicle cooperation also increases vehicle-to-everything (V2X) communication demand, processing overhead, and operational resou...
M. Alrashidi, Wajih Abdallah· Italian National Conference...· 0 citations
Collaborative perception improves autonomous perception by sharing intermediate Bird's-Eye-View (BEV) features across connected agents, but dense feature exchange is difficult to deploy under strict Vehicle-to-Everything (V2X) bandwidth limits. Existing efficient methods typically either compress the full feature map u...
Vehicle-to-Vehicle (V2V) cooperative perception enhances autonomous driving by enabling vehicles to share information beyond their direct line of sight. However, existing V2V datasets are limited by a small number of participating agents, static collaborator selection strategies, and a significant domain gap between si...
Yu-Lu Wu, Chao Wei, Ju-Jun Cheng et al.· 0 citations
This work presents a conceptual framework for Collaborative Joint Perception and Prediction (Co-P&P) that improves motion prediction of surrounding road users, thereby enhancing situational awareness in complex and dynamic traffic environments.
Lei Wan, Hannan Ejaz Keen, Alexey V. Vinel· 0 citations
Emitter spectrum cognition is a critical capability for electromagnetic environment awareness, where heterogeneous sensing observations must be integrated to support reliable emitter state estimation and signal behavior understanding. This paper investigates the potential of large language models (LLMs) for AI-based em...
Hai-Yang Sun, Yao Zhou, Lin Gao et al.· 2026 IEEE/CIC International...· 0 citations
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