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.

翻译：本文提出了一种面向学习增强型自动驾驶车辆的新型数字孪生原型。该数字孪生的主要目标是实现交通标志识别与车道保持功能。数字孪生架构基于联合仿真，并采用了功能样机接口与SystemC事务级建模标准。该数字孪生包含四个客户端：（i）基于Amesim工具设计的车辆模型；（ii）基于Prescan开发的环境模型；（iii）基于机器人操作系统设计的车道保持控制器；（iv）基于BIP（行为、交互、优先级）形式化建模语言开发的感知与速度控制模块。这些客户端通过数字孪生平台PAVE360-Veloce系统互连（PAVE360-VSI）进行交互。PAVE360-VSI作为联合仿真协调器，负责通过服务器实现各客户端的同步、互连与数据交换。该服务器在建立不同客户端连接的同时，确保遵循以太网协议。最后，本文通过数字孪生仿真示例进行说明，并提出未来工作建议。