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%.