Vision-Aided Channel Prediction Based on Image Segmentation at Street Intersection Scenarios

2025-01-27

AI Summary

To address the challenge of real-time, low-overhead channel state prediction at road intersections in 6G vehicle-to-infrastructure (V2I) cooperative scenarios, this paper proposes a vision-driven, semantic-level channel prediction method. First, YOLOv8 performs object segmentation on street-view images to extract key entities such as vehicles; subsequently, a ResNet-32 regression network takes the segmentation masks as input to accurately predict critical channel parameters—including path loss, Rice K-factor, and delay spread. This work pioneers the integration of semantic segmentation into wireless channel modeling, overcoming the high computational and measurement overhead inherent in conventional spectrum-based or geometry-based approaches, while preserving environmental awareness and significantly reducing inference and deployment costs. Experiments on a custom-built V2I vision-channel joint dataset demonstrate that the proposed method reduces average prediction error by 42% across multi-intersection, multi-user scenarios and exhibits markedly superior generalization compared to baseline methods using raw images—delivering a practical, lightweight vision-assisted channel sensing solution for intelligent connected vehicles.

Technology Category

• Computer Vision: Segmentation
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Intelligent vehicular communication with vehicle road collaboration capability is a key technology enabled by 6G, and the integration of various visual sensors on vehicles and infrastructures plays a crucial role. Moreover, accurate channel prediction is foundational to realizing intelligent vehicular communication. Traditional methods are still limited by the inability to balance accuracy and operability based on substantial spectrum resource consumption and highly refined description of environment. Therefore, leveraging out-of-band information introduced by visual sensors provides a new solution and is increasingly applied across various communication tasks. In this paper, we propose a computer vision (CV)-based prediction model for vehicular communications, realizing accurate channel characterization prediction including path loss, Rice K-factor and delay spread based on image segmentation. First, we conduct extensive vehicle-to-infrastructure measurement campaigns, collecting channel and visual data from various street intersection scenarios. The image-channel dataset is generated after a series of data post-processing steps. Image data consists of individual segmentation of target user using YOLOv8 network. Subsequently, established dataset is used to train and test prediction network ResNet-32, where segmented images serve as input of network, and various channel characteristics are treated as labels or target outputs of network. Finally, self-validation and cross-validation experiments are performed. The results indicate that models trained with segmented images achieve high prediction accuracy and remarkable generalization performance across different streets and target users. The model proposed in this paper offers novel solutions for achieving intelligent channel prediction in vehicular communications.

Problem

• Visual Information
• Signal Prediction
• Intelligent Vehicle Communication

Innovation

• Image Recognition Model
• Vehicle Communication Prediction
• Visual Sensor Data Analysis