Data-Driven Optimization of IEEE 802.1Qcr Asynchronous Traffic Shaper Parameters for Automotive Networks

Abstract

Ensuring deterministic and reliable communication is essential for in-vehicle networks supporting autonomous driving and safety-critical functions. Time-Sensitive Networking has emerged as a key enabler for such systems. Among its mechanisms, the IEEE 802.1Qcr Asynchronous Traffic Shaper (ATS) offers fine-grained traffic control without requiring global time synchronization. However, the practical deployment of ATS in Automotive Ethernet networks remains challenging due to the difficulty of parameter configuration. The performance of ATS strongly depends on the appropriate setting of key parameters such as the Committed Information Rate (CIR) and Committed Burst Size (CBSz), which are highly sensitive to both network topology and traffic workload. Conventional approaches relying on static configuration or empirical tuning may face difficulties in ensuring QoS when network conditions change. This paper proposes a method for automated, high-precision optimization of ATS parameters in automotive networks. We analyze the impact of key parameters—CIR and CBSz—on delay and frame loss, and develop a machine learning model to select optimal settings under dynamic traffic conditions. Our results reveal that proper ATS parameter configuration is essential for deterministic latency and reliability in Automotive Ethernet networks. CIR mainly governs bandwidth, affecting queuing delay and frame loss, while CBSz balances delay reduction against burst-induced congestion. Furthermore, tree-based ensemble models such as LightGBM and Gradient Boosting achieved high prediction accuracy and QoS satisfaction under varying traffic conditions.