Context aware filtering cuts communication in vehicle safety systems
Frugal Collective Perception: Context-Aware Adaptive Reporting for Safety-Critical C-ITS
Networking and Internet Architecture
Summary
Cars that share what they see to stay safe can overwhelm communication networks with too much information. The authors propose a smart filter that sends only the most important safety details depending on the situation. This helps avoid network jams while still keeping all vehicles aware of critical dangers. Their tests show the new method can reduce data traffic by over 90% without hurting safety. This approach improves how well cooperative vehicle systems can work together on different hardware.
What this means in practice
- •For automotive system integrators: Implement context-aware message filtering in connected vehicle platforms to maintain safety without overloading communication networks.
- •For transportation network operators: Deploy adaptive collective perception message protocols to improve scalability and safety on vehicle-to-everything communication networks.
Authors
Romain Tessier, Bruno Monsuez, Oyunchimeg Shagdar, Adriana Tapus
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.