Frugal Collective Perception: Context-Aware Adaptive Reporting for Safety-Critical C-ITS

2026-09-22

AI Summary

为解决C-ITS中集体感知服务的安全与可扩展性问题，提出一种基于情境自适应过滤的方法，动态调整信息内容和传输频率，减少通信量并保持安全性。

Abstract

Ensuring safety and scalability in Collective Perception Service (CPS) remains a key challenge for Cooperative Intelligent Transport Systems (C-ITS). Conventional CPS enhances perception by broadcasting Collective Perception Messages (CPMs). However, its reliance on transmitting a potentially large volume of context-irrelevant information at high frequency leads to network congestion, processing delays, and poor scalability. We propose a Context-Aware Adaptive Filter that dynamically adjusts CPM content and transmission frequency based on contextual relevance and situational criticality. By prioritizing safety-critical objects and interactions, the proposed approach prevents information overload while preserving timely updates for decision-making. An end-to-end SUMO--Artery simulation evaluates safety, decision-making efficiency, and communication cost under different computational capacity tiers and transmission rates. Results show that the proposed adaptive filtering mechanism achieves safety performance comparable to conventional CPMs transmitted at the maximum allowed frequency (10~Hz), while reducing communication volume by over 93\% and preventing queue saturation. This demonstrates that context-aware adaptivity enables CPS to remain both scalable and safety-compliant across heterogeneous computing platforms.

Problem

• Collective Perception Service
• network congestion
• processing delays
• scalability

Innovation

• Context-Aware Adaptive Filter
• Collective Perception Messages (CPMs)
• safety-critical objects
• information overload
• scalability