Vehicle–Road–Cloud Collaborative Perception: Resource and Intelligence Optimization
Source abstract
Abstract
Vehicle–road–cloud collaborative perception improves perception performance via multi-agent information sharing and data fusion, but it faces coupled trade-offs among perception accuracy, computing resources, and communication bandwidth. Optimizing agents’ intelligence or underlaying resources alone fails to resolve this conflict, limiting collaboration efficiency. We propose C4I-JO, a joint resource and intelligence optimization method for vehicle–road–cloud collaborative perception. We employ slimmable networks to achieve intelligent elasticity. Based on these, C4I-JO jointly optimizes four key dimensions to minimize resource consumption while meeting accuracy and latency constraints, including collaborative mechanisms to cut redundant communication, resource allocation to avoid supply–demand bottlenecks, intelligent elasticity to balance accuracy and resources, and computation offloading to reduce local burden. We propose a two-layer iterative decoupling algorithm that addresses the optimization problem. Specifically, the outer level leverages Second-Order Cone Programming (SOCP) and the interior-point method, while the inner level utilizes a Genetic Algorithm (GA). Simulations show that C4I-JO outperforms baselines in both resource efficiency and perception quality.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Vehicular Ad Hoc Networks (VANETs) · Autonomous Vehicle Technology and Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
China Unicom Smart Connection Technology Limited, Beijing 101599, China
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Funding
National Key Research and Development Program of China
References (28)
Show source-provided references
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