By sharing traffic flow information, vehicle motion and road traffic control can be jointly optimized for improving overall traffic efficiency 3, 4.

In order to achieve efficient autonomous driving, current trend is to connect vehicles to each other as well as to connect vehicles and road facilities 1, 2. However, the perception capability of an individual vehicle is limited to its surroundings, which has limited benefits for improving the operational efficiency of large-scale traffic. An important research direction of autonomous driving is the realization of single-vehicle self-driving. With the progress of computing power and communication technology, real-time environmental perception and path planning can be realized in recent years, which promotes the rapid development of autonomous driving technology. The effectiveness of the digital twin building is verified based on the real-world traffic data. Starting from a paired radar-camera sensing system, a single-site radar-camera fusion framework is proposed, and then using the definition of a unified coordinate system, the traffic flow data between multiple sites is combined to form a dynamic real-time traffic flow digital twin model. In this paper, an end-to-end digital twin system implementation approach is proposed for highway scenarios. The current mainstream traffic sensors, namely radar and camera, have their own advantages, and the fusion of these two sensors can provide more accurate traffic flow data for the generation of digital twin model. The digital twin can enable the traffic system not only to use past and present information, but also to predict traffic conditions, providing more effective optimization for autonomous driving and intelligent transportation, so as to make long-term rational planning of the overall traffic state and enhance the level of traffic intelligence.

When the sensing technology is combined with the internet of vehicles, a sensor network all over the road can provide a large-scale of traffic flow data, thus providing a basis for building a traffic digital twin model. Eng., 18, 539 (2001).Autonomous driving is gradually moving from single-vehicle intelligence to internet of vehicles, where traffic participants can share the traffic flow information perceived by each other. H., “Characteristics of electrostatic cyclone/bac filter with inlet types (lab and pilot scale),” Korean J. T., “Size distribution of airborne dust in mines, in aerosols: In the mining and industrial work environments,” V.

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