Publicação
Integrated Perception, Communication, and Computation for Autonomous Vehicle and Road Infrastructure Network
| Resumo: | Vehicle-to-Infrastructure (V2I) collaboration constitutes an emerging paradigm for advancing autonomous driving. However, the integrated collaboration of perception, communication, and computation within V2I system remains a critical challenge. To address it, we propose a Software-Defined Network (SDN)-based collaborative approach for Autonomous Vehicle and Road Infrastructure Network (AVRIN). The architecture designates road infrastructures as road nodes and autonomous vehicles as dynamic vehicle nodes, establishing AVRIN through SDN. The control plane dynamically maintains global network topology and distributed flow tables by continuously evaluating node accessibility, while the forwarding plane is responsible for packet transmission via the OpenFlow protocol. In the perception module, road nodes divide the perception range into spatial units, whereas vehicle nodes dynamically align these units with their drivable areas across temporal sequences. Through coordinated communication and computation modules, road nodes strategically allocate dedicated bandwidth and computational resources. Building on this approach, we develop a particle swarm-based multi-objective optimization algorithm to achieve balanced co-optimization across perception, communication, and computation. Experimental validation demonstrates its superior collaborative Bird's Eye View (BEV) detection performance on the V2X-Sim 2.0 dataset, outperforming existing approaches by 10.37% in mean Average Precision. Furthermore, evaluations on the newly collected Jiading dataset, from a real-world urban roadway, confirm the approach's robustness with 1.823-second computation time under dynamic network conditions. |
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| Autores principais: | Yang, Lu |
| Outros Autores: | Cheng, Jiujun; Zhou, Mengchu; Liu, Cong; Ni, Zhangkai; Xie, Mande; Gao, Shangce |
| Assunto: | Autonomous vehicle road infrastructure Vehicle-to-Infrastructure (V2I) collaboration Software-Defined Networks (SDN) multi-objective optimization Software Computer Networks and Communications Electrical and Electronic Engineering SDG 9 - Industry, Innovation, and Infrastructure SDG 11 - Sustainable Cities and Communities |
| Ano: | 2026 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Vehicle-to-Infrastructure (V2I) collaboration constitutes an emerging paradigm for advancing autonomous driving. However, the integrated collaboration of perception, communication, and computation within V2I system remains a critical challenge. To address it, we propose a Software-Defined Network (SDN)-based collaborative approach for Autonomous Vehicle and Road Infrastructure Network (AVRIN). The architecture designates road infrastructures as road nodes and autonomous vehicles as dynamic vehicle nodes, establishing AVRIN through SDN. The control plane dynamically maintains global network topology and distributed flow tables by continuously evaluating node accessibility, while the forwarding plane is responsible for packet transmission via the OpenFlow protocol. In the perception module, road nodes divide the perception range into spatial units, whereas vehicle nodes dynamically align these units with their drivable areas across temporal sequences. Through coordinated communication and computation modules, road nodes strategically allocate dedicated bandwidth and computational resources. Building on this approach, we develop a particle swarm-based multi-objective optimization algorithm to achieve balanced co-optimization across perception, communication, and computation. Experimental validation demonstrates its superior collaborative Bird's Eye View (BEV) detection performance on the V2X-Sim 2.0 dataset, outperforming existing approaches by 10.37% in mean Average Precision. Furthermore, evaluations on the newly collected Jiading dataset, from a real-world urban roadway, confirm the approach's robustness with 1.823-second computation time under dynamic network conditions. |
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