一种用于具备学习能力的自动驾驶车辆交通标志识别的数字孪生原型
A Digital Twin prototype for traffic sign recognition of a learning-enabled autonomous vehicle
AI总结：
本文提出一种用于具备学习能力的自动驾驶车辆的数字孪生原型，采用协同仿真架构集成车辆、环境、车道保持控制器及BIP感知模块，以实现交通标志识别与车道保持功能。
摘要：
本文提出了一种用于具备学习能力的自动驾驶车辆的新型数字孪生原型。该数字孪生的首要目标是执行交通标志识别和车道保持。数字孪生架构依赖于协同仿真，并采用Functional Mock-up Interface和SystemC Transaction Level Modeling标准。该数字孪生由四个客户端组成：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Robot Operating System中设计的车道保持控制器，以及iv) 使用BIP（Behavior, Interaction, Priority）形式化建模语言开发的感知与速度控制模块。这些客户端与数字孪生平台PAVE360-Veloce System Interconnect（PAVE360-VSI）接口。PAVE360-VSI作为协同仿真编排器，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，并确保遵循Ethernet协议。最后，我们展示了说明性的数字孪生仿真结果及对未来工作的建议。
英文摘要：
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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