Self‐Optimized Computational Resource Allocation for Enhanced Perception in Intersection Blind Spots

Blind spots represent a critical challenge to maintaining traffic safety. The development and deployment of intelligent and connected vehicle technologies have resulted in significant enhancements to traffic safety. However, blind spots are still a thorny issue, especially on traffic safety at intersections because of the complexity of their reasoning environment. Despite these advancements, blind spots remain a significant challenge for traffic safety, particularly at intersections, where the complex driving environment hinders accurate perception. In this paper, we introduce an innovative architecture that leverages self‐optimizing computational (SOC) network resources to improve the accuracy and efficiency of blind spot detection for vehicles at intersections. This approach tackles two critical challenges: excessive data volumes causing significant transmission delays and insufficient data leading to a deficiency in essential features required for accurate vehicle state assessment. Through dynamic allocation of network resources and real‐time performance optimization, it significantly enhances perception and thereby improves traffic safety. (1) Grounded in a comprehensive analysis of real‐world conditions, this method enhances performance by focusing on critical areas, optimizing information packaging, and efficiently utilizing communication resources; and (2) this method employs dynamic analysis of blind spots to automatically optimize the allocation of computational resources, ensuring efficient and real‐time performance adjustments. To evaluate the proposed perceptual architecture, we validated it using the DAIR‐V2X dataset, a benchmark for real‐world vehicular infrastructure collaboration, achieving an average precision (AP) of 67.20% at a communication rate of 5.09%.