Time-Sensitive Network Simulation for In-Vehicle Ethernet Using SARSA Algorithm

Abstract

In order to more accurately analyze the problem of time delay simulation and calculation in the time-sensitive network (TSN) of vehicular Ethernet, a TSN reservation class data delay analysis model improved based on the State–Action–Reward–State–Action (SARSA) reinforcement learning algorithm is proposed. Firstly, the TSN data queue forwarding delay model and reservation class data delay analysis intelligent body model are established, then the TSN traffic scheduling mechanism is improved by the SARSA reinforcement learning algorithm, and the improved TSN network reservation class data analysis model is established for the uncertainty of traffic scheduling in the network; finally, the fitting performance of the proposed method is verified by simulation and experimental validation. The results show that the deviation between the two is less than 5% under different BE loads, i.e., the established reservation class data delay analysis model is able to correctly fit the scheduling mechanism of the vehicle-mounted TSN network, which proves the reasonableness of the model simulation.