面向自动驾驶的智能出行数字孪生：设计与概念验证
Smart Mobility Digital Twin for Automated Driving: Design and Proof-of-Concept
AI总结：
本文提出基于数字孪生的智能出行平台，利用云/边缘计算和协同环境感知实现实时交通监控与大规模路径规划，并通过真实车辆概念验证其功能与低时延性能。
摘要：
在过去十年中，智能出行和智能车辆引起了越来越多的关注，因为它们有望在未来构建一个高效且安全的交通系统。与此同时，数字孪生作为一项新兴技术，将在自动驾驶和智能交通系统中发挥重要作用。本文将该技术应用于设计一个智能出行平台，提供大规模路径规划服务。利用感知技术以及云/边缘计算，我们构建了一个数字孪生系统模型，能够实时反映真实世界中的静态和动态对象。借助该智能出行平台，我们通过协同环境感知实现交通监控和路径规划，帮助自动驾驶车辆避开拥堵。我们在真实交通环境中使用真实车辆进行了概念验证测试，以验证所提出平台的功能和时延性能。
英文摘要：
During the past decade, smart mobility and intelligent vehicles have attracted increasing attention, because they promise to create a highly efficient and safe transportation system in the future. Meanwhile, digital twin, as an emerging technology, will play an important role in automated driving and intelligent transportation systems. This technology is applied in this paper to design a platform for smart mobility, providing large-scale route planning services. Utilizing sensing technologies and cloud/edge computing, we build a digital twin system model that reflects the static and dynamic objects from the real world in real time. With the smart mobility platform, we realize traffic monitoring and route planning through cooperative environment perception to help automated vehicles circumvent jams. A proof-of-concept test with a real vehicle in real traffic is conducted to validate the functions and the delay performance of the proposed platform.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
