The cutting-edge technology of connected and automated vehicles (CAVs) will advance transportation systems for the foreseeable future. CAVs are expected to maintain fully automated judgment and manipulation without human intervention and, additionally, create safer driving and smarter traffic management. Digital twins (DTs) are the quiet but powerful forces enabling these new possibilities behind the scenes. In this article, we design a DT network (DTN) consisting of connected DTs to help CAVs in terms of ubiquitous perception, adaptive path planning, and precise motion control. Heterogeneous learning models and diverse learning methods are employed at different scales of solution, progressing toward specificity, adaptation, and accuracy. Qualitative evaluation of the proposed system is performed with the final goal of demonstrating the DTN’s assistance in improving the performance and effectiveness of CAVs, ultimately leading to a safer, more efficient, and more sustainable transportation system.

中文翻译：

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数字孪生网络如何在互联和自动化车辆中发挥良好作用：联合感知、规划和控制

联网和自动驾驶车辆 (CAV) 的尖端技术将在可预见的未来推动交通系统的发展。 CAV 有望在无需人工干预的情况下保持完全自动化的判断和操纵，此外还能实现更安全的驾驶和更智能的交通管理。数字孪生 (DT) 是一种安静但强大的力量，在幕后实现这些新的可能性。在本文中，我们设计了一个由连接的DT组成的DT网络（DTN），以帮助CAV实现普遍感知、自适应路径规划和精确运动控制。在不同规模的解决方案中采用异构学习模型和多样化的学习方法，朝着特异性、适应性和准确性的方向发展。对拟议系统进行定性评估的最终目标是展示 DTN 在提高 CAV 性能和有效性方面的帮助，最终形成更安全、更高效和更可持续的交通系统。