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

arXiv · 2402.09097

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

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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.

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Mohamed AbdElSalam, Loai Ali, Saddek Bensalem, Weicheng He, Panagiotis Katsaros, Nikolaos Kekatos, Doron Peled, Anastasios Temperekidis, Changshun Wu. 2024-02-14. A Digital Twin prototype for traffic sign recognition of a learning-enabled autonomous vehicle. https://arxiv.org/abs/2402.09097

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