A Digital Twin prototype for traffic sign recognition of a learning-enabled autonomous vehicle

Mohamed AbdElSalam, Loai Ali, Saddek Bensalem, Weicheng He, Panagiotis Katsaros, Nikolaos Kekatos, Doron Peled, Anastasios Temperekidis, Changshun Wu

Research output: Contribution to journalConference articlepeer-review

Abstract

In this paper, we present a novel digital twin prototype for a learning-enabled self-driving vehicle. The primary objective of this digital twin is to perform traffic sign recognition and lane keeping. The digital twin architecture relies on co-simulation and uses the Functional Mock-up Interface and SystemC Transaction Level Modeling standards. The digital twin consists of four clients, i) a vehicle model that is designed in Amesim tool, ii) an environment model developed in Prescan, iii) a lane-keeping controller designed in Robot Operating System, and iv) a perception and speed control module developed in the formal modeling language of BIP (Behavior, Interaction, Priority). These clients interface with the digital twin platform, PAVE360-Veloce System Interconnect (PAVE360-VSI). PAVE360-VSI acts as the co-simulation orchestrator and is responsible for synchronization, interconnection, and data exchange through a server. The server establishes connections among the different clients and also ensures adherence to the Ethernet protocol. We conclude with illustrative digital twin simulations and recommendations for future work.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume3645
StatePublished - 2023
Event16th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling, EDEWC 2023 - Vienna, Austria
Duration: 28 Nov 20231 Dec 2023

Keywords

  • FMI
  • SystemC
  • YOLOX
  • autonomous vehicle
  • co-simulation
  • digital twin
  • lane keeping
  • perception

All Science Journal Classification (ASJC) codes

  • General Computer Science

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