## 1 Introduction  
With the rapid advancement in communication, computer, and sensor technology, the vehicle-road-cloud cooperation cyber-physical system (VRCCPS) has undergone substantial development, which contributes to the maturity of autonomous vehicles (AVs) [1]. Within VRCCPS, real-time traffic information is collected by both onboard and roadside sensors and aggregated on the dynamic high-definition (HD) map platform for building the digital twin of real traffic scenarios [2]. On this basis, VRCCPS conducts predictive motion planning for AVs within the digital twin and employs the cloud control platform to guide real-world vehicles to improve traffic safety and efficiency. Thus, it is crucial for VRCCPS to accurately predict future traffic states based on the traffic environment perception provided by HD maps. In contrast to conventional traffic detection systems, VRCCPS employs roadside perception devices to capture real-time motion information of traffic participants at a high frequency and aggregates these perceptions on the dynamic layer of HD maps. Therefore, the HD maps in VRCCPS can provide both real-time vehicle state information and comprehensive semantic information about vehicles and roads. However, there is a lack of research on leveraging HD maps for traffic state predictions, especially in complex urban traffic scenarios. Thus, we propose a model that utilizes structured data from HD maps to forecast future traffic states. Currently, the mainstream frameworks for HD map creation include Lanelet2, OpenDRIVE, and Apollo Maps [3]. These frameworks typically partition continuous road space into a series of lane segments with accurate geometric shapes and abundant semantic information, aiding in precise vehicle localization and motion planning. HD maps consist of multiple layers that successively update the states of various traffic objects at different frequencies [4]. Based on real-time traffic information from HD maps, the VRCCPS needs to predict traffic state evolution to construct space-time corridors from unoccupied lane segments in the future for motion planning tasks [5], as illustrated in Figure 1. Thus, the prediction model proposed in this paper must forecast the future traffic states of the whole lane segments during the prediction horizon based on discrete spatial-temporal traffic information from HD maps. As a classical traffic model, the cell transmission model (CTM) can forecast future traffic states in discrete space-time. In CTM, the road space is divided into a set of cells with various traffic states, and the transmission rules of vehicles between adjacent cells are determined by the fundamental diagram of traffic flow [6]. Based on transmission rules, CTM can successively update the future states of the entire cells. However, the fundamental diagram of traffic flow fails to sufficiently leverage the real-time vehicle state and semantic information from HD maps, such as the azimuth and vehicle-type attribute, thereby limiting further improvement in the prediction accuracy of CTM. Thus, the model proposed in this paper employs the discrete spatial-temporal structure of CTM and innovates the method for updating the traffic state of lane segments. By combining conventional CTM and Transformer networks, we propose a novel prediction model known as Cell Transformer (CeT). CeT can utilize the structured data  
Figure 1: A three-dimensional representation in which the horizontal axis denotes lanes, the vertical axis indicates time, and the depth axis represents the road. The ego vehicle and background vehicles are situated along lane segments. The orange space–time corridor delineates the unoccupied future lane segments available for AV, while the gray curve illustrates the optimized planning trajectory within this corridor. This visualization emphasizes the consideration of future traffic states, which is essential for ensuring safe and efficient trajectory planning. from HD maps to forecast future traffic states.