Design Methodology of Automotive Time-Sensitive Network System Based on OMNeT++ Simulation System

Sensors (Basel). 2022 Jun 17;22(12):4580. doi: 10.3390/s22124580.

Abstract

Advances in automotive technology require networks to support a variety of communication requirements, such as reliability, real-time performance, low jitter, and strict delay limits. Time-Sensitive Network (TSN) is a keyframe transmission delay-guaranteed solution based on the IEEE 802 architecture of the automotive Ethernet. However, most of the existing studies on automotive TSN performance are based on a single mechanism, lacking a complete and systematic research tool. At the same time, the design method should be considered from a global perspective when designing an automotive TSN system, rather than only considering a single mechanism that TSN applies to. This paper discusses the correspondence between traffic types and automotive scenarios and proposes a methodology to target the delay constraint of traffic types as the design goal of automotive TSN networks. To study the performance of automotive TSN under different mechanisms such as time-aware shaper (TAS), credit-based shaper (CBS), cyclic queuing and forwarding (CQF), etc., this paper also develops a systematic automotive TSN simulation system based on OMNeT++. The simulation system plays a crucial role in the whole methodology, including all applicable TSN standards for the automotive field. Lastly, a complex automotive scenario based on zonal architecture provided by a major motor company in Shanghai is analyzed in the simulated system; verifying TSN can guarantee real-time performance and reliability of the in-vehicle network.

Keywords: OMNeT++; automotive Ethernet; communications simulation; time-sensitive network; traffic scheduling.

Grants and funding

This research was funded by the Shanghai Automotive Industry Science and Technology Development Foundation (1806) and the Perspective Study Funding of Nanchang Automotive Institute of Intelligence and New Energy, Tongji University (TPD-TC202110-14).