Quality over Quantity: Value-Driven Distributed Congestion Control for the Collective Perception Service

2026-07-20

AI Summary

This work addresses the limitation of existing vehicular congestion control mechanisms, which often neglect the value of information (VoI) associated with perceived objects, thereby failing to prioritize high-value data under channel constraints. To overcome this, the paper proposes a distributed, infrastructure-based congestion control approach that uniquely integrates fine-grained VoI-driven object selection with per-bit VoI-aware rate control. This coupling enables dynamic optimization of high-VoI perception message dissemination while maintaining channel load close to the target channel busy ratio (CBR). Experimental results demonstrate that the proposed method significantly enhances the delivery efficiency of critical perception information, preserving substantially more high-VoI objects compared to state-of-the-art schemes, and effectively adapts to heterogeneous and dynamic traffic environments.

Abstract

While the Collective Perception Service (CPS) enables the exchange of sensor information among Intelligent Transport System Stations (ITS-S'), frequent transmission of Collective Perception Messages (CPMs), their highly variable size, and load from other vehicular services can cause severe channel congestion. Existing Distributed Congestion Control (DCC) Access layer mechanisms typically regulate channel load without considering the relative importance of the objects carried in CPMs. This limits their ability to preserve high-value information under constrained radio resources. More recently, Facilities layer DCC mechanisms attempt to prioritise high value objects within the specified radio resource limits but may not operate well in heterogeneous environments where the number of sensed objects and their importance can vary significantly over time or between ITS-S'. This paper proposes a value-based DCC Facilities layer 'quality' selector that couples a Value of Information (VoI) per bit rate controller with object-level selection. It is benchmarked against state of the art approaches from standards and the literature, with results showing that the proposed method maintains channel load near the target CBR while retaining more high-VoI objects than state of the art approaches, thereby improving the dissemination of perception-critical information.

Problem

• Collective Perception Service
• Distributed Congestion Control
• Value of Information
• Channel Congestion
• Intelligent Transport Systems

Innovation

• Value of Information
• Distributed Congestion Control
• Collective Perception Service
• Facilities layer
• Object-level selection