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.

本文提出了一个新颖的数字孪生原型，用于学习能力的自动驾驶车辆，其主要目标是进行交通标志识别和车道保持。数字孪生架构依赖于联合仿真，采用了功能模拟接口和 SystemC 事务级建模标准。数字孪生由四个客户端组成，即 i）在 Amesim 工具中设计的车辆模型，ii）在 Prescan 中开发的环境模型，iii）在机器人操作系统中设计的车道保持控制器，以及 iv）在 BIP（行为、交互、优先级）的形式建模语言中开发的感知和速度控制模块。这些客户端与数字孪生平台 PAVE360-Veloce System Interconnect（PAVE360-VSI）进行接口。PAVE360-VSI 充当联合仿真协调器，负责通过服务器进行同步、互连和数据交换。服务器在不同客户端之间建立连接，并确保遵守以太网协议。最后，我们给出了数字孪生仿真和未来工作的建议。