Zhouping Zhang

Time-Sensitive Networking (TSN) has emerged as a critical communication protocol for automotive Ethernet to support the high-bandwidth, real-time, and deterministic transmission requirements of next-generation in-vehicle networks. However, a clear and effective TSN mechanism combination tailored to the mixed and bursty traffic characteristics of automotive scenarios remains lacking. To address this issue, this paper proposes a combined TSN scheduling mechanism for automotive scenarios. The highest-priority traffic is scheduled by class-based Time-Aware Shaper (TAS), periodic bursty sensor traffic is shaped by Credit-Based Shaper (CBS), and medium-priority traffic adopts Multi-Cyclic Queueing and Forwarding (MCQF). Based on Compositional Performance Analysis (CPA), this paper derives the worst-case latency upper bound expressions for CQF streams and optimizes the schedulability analysis to reduce conservative errors. Simulation verifies that the theoretically calculated bounds cover the maximum simulation latency, and the optimized analysis reduces conservatism, with peak conservative error of 3.07% in the ring scenario and 10.59% in the automotive scenario. Compared with the strict priority and TAMCQF (a combination of TAS and Multi-CQF), the proposed mechanism combination suppresses the latency jitter of mixed traffic, mitigates long-duration blocking of medium-priority traffic caused by high-priority burst data, and provides reliable deterministic transmission guarantees for automotive in-vehicle networks.

With the rapid advancement of intelligent and connected vehicles, in-vehicle networks face increasing demands for high-capacity data processing and transmission. Traditional static scheduling methods in Time Sensitive Network (TSN) can no longer satisfy the stringent real-time requirements of dynamic environments. The scheduling of time-sensitive traffic is typically addressed through offline computation, producing a mathematically constrained schedule that is subsequently deployed to end nodes or switches for configuration. However, such static deployment fails to accommodate dynamic scenarios. Consequently, this study proposes a dynamic incremental scheduling framework for TSN based on Deep Reinforcement Learning (DRL). The proposed framework integrates static global optimization with rapid dynamic adaptability. In the static phase, a Deep Q-Network (DQN) learns the priority ordering of time-sensitive flow scheduling under the given network state, thereby guiding a heuristic scheduler to efficiently allocate link resources. In the dynamic phase, an incremental scheduling strategy locally reconfigures resources for newly introduced flows without affecting existing schedules, thereby enhancing responsiveness and feasibility. Furthermore, this study proposes a load-balancing-guided routing set generation method that jointly optimizes path hop count and link load, enabling coordinated scheduling and routing. Comparative analysis under various network load scenarios demonstrates that the proposed method significantly reduces the response time of dynamic flow scheduling by 35% compared to the Satisfiability Modulo Theories (SMT) algorithm, while maintaining scheduling feasibility.