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Area of Science:

  • Automotive Engineering
  • Computer Networking
  • Cyber-Physical Systems

Background:

  • Autonomous driving systems generate substantial data traffic, requiring high-bandwidth in-vehicle communication networks.
  • Traditional automotive networks face challenges in meeting the demands of advanced driver-assistance systems (ADAS) and autonomous features.
  • Time-Sensitive Networking (TSN) standards, developed by IEEE 802.1, offer a promising solution for deterministic and reliable in-vehicle communication.

Purpose of the Study:

  • To develop and validate a TSN integrated environment simulator for evaluating emerging TSN standards in automotive applications.
  • To analyze the performance of TSN, particularly preemption, for autonomous driving traffic within an in-vehicle network (IVN).
  • To minimize performance discrepancies and reduce End-to-End latency for critical automotive data transmission.

Main Methods:

  • Development of a TSN integrated environment simulator to model and test key TSN functions.
  • Analysis of autonomous driving traffic patterns against TSN transmission requirements and preemption mechanisms.
  • Design of an IVN model for autonomous vehicles and performance testing with simulated sensor and ECU data.

Main Results:

  • The simulator successfully verified that autonomous driving traffic meets TSN transmission requirements within the IVN.
  • Implementation of TSN preemption demonstrated a reduction in overall End-to-End delay for critical data.
  • An optimal guard band size for TSN preemption was determined for autonomous vehicle applications.

Conclusions:

  • The developed TSN simulator effectively validates the integration of evolving TSN standards for in-vehicle networks.
  • TSN technology, especially preemption, is crucial for ensuring reliable and low-latency communication in autonomous vehicles.
  • The findings provide a foundation for deploying robust TSN-based IVNs to support the future of autonomous driving.