DRIVE：协同智能交通系统的数字网络预言机
DRIVE: A Digital Network Oracle for Cooperative Intelligent Transportation Systems
摘要：
在一个人工智能彻底改变推理、预测和决策任务的世界里，数字孪生成为改变游戏规则的工具。一个恰当的例子是合作智能交通系统（C-ITS）的开发和优化：网络物理数字基础设施和（半）自动移动性的融合。本文介绍了用于自驱动智能车辆（DRIVE）的数字孪生。开发的框架解决了传统的车辆和网络模拟器的缺点。它提供了一个灵活的，模块化的，可扩展的实现，以确保大规模的，全市范围的实验与适度的计算成本。我们的数字孪生的定义特征是一个独特的架构，允许提交顺序查询，数字孪生提供即时响应与“世界的状态”，因此是一个甲骨文。通过与外部智能代理和真实移动轨迹的双向交互，DRIVE为基于机器学习的C-ITS解决方案的开发、训练和优化提供了环境。
英文摘要：
In a world where Artificial Intelligence revolutionizes inference, prediction and decision-making tasks, Digital Twins emerge as game-changing tools. A case in point is the development and optimization of Cooperative Intelligent Transportation Systems (C-ITSs): a confluence of cyber-physical digital infrastructure and (semi)automated mobility. Herein we introduce Digital Twin for self-dRiving Intelligent VEhicles (DRIVE). The developed framework tackles shortcomings of traditional vehicular and network simulators. It provides a flexible, modular, and scalable implementation to ensure large-scale, city-wide experimentation with a moderate computational cost. The defining feature of our Digital Twin is a unique architecture allowing for submission of sequential queries, to which the Digital Twin provides instantaneous responses with the "state of the world", and hence is an Oracle. With such bidirectional interaction with external intelligent agents and realistic mobility traces, DRIVE provides the environment for development, training and optimization of Machine Learning based C-ITS solutions.
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。
