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Intelligent connected vehicles (ICVs) have played an important role in improving the intelligence degree of transportation systems, and improving the trajectory prediction capability of ICVs is beneficial for traffic efficiency and safety. In this paper, a real-time trajectory prediction method based on vehicle-to-everything (V2X) communication is proposed for ICVs to improve the accuracy of their trajectory prediction. Firstly, this paper applies a Gaussian mixture probability hypothesis density (GM-PHD) model to construct the multidimension dataset of ICV states. Secondly, this paper adopts vehicular microscopic data with more dimensions, which is output by GM-PHD as the input of LSTM to ensure the consistency of the prediction results. Then, the signal light factor and Q-Learning algorithm were applied to improve the LSTM model, adding features in the spatial dimension to complement the temporal features used in the LSTM. When compared with the previous models, more consideration was given to the dynamic spatial environment. Finally, an intersection at Fushi Road in Shijingshan District, Beijing, was selected as the field test scenario. The final experimental results show that the GM-PHD model achieved an average error of 0.1181 m, which is a 44.05% reduction compared to the LiDAR-based model. Meanwhile, the error of the proposed model can reach 0.501 m. When compared to the social LSTM model, the prediction error was reduced by 29.43% under the average displacement error (ADE) metric. The proposed method can provide data support and an effective theoretical basis for decision systems to improve traffic safety. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of vehicles has brought significant challenges to energy conservation and environmental sustainability. To solve these problems, various frameworks and models based on intelligent connected vehicles (ICVs) have been identified for road capacity improvement and fuel consumption reduction. In this paper, an eco-driving controller with ICVs was first proposed by combining vehicular dynamics with wireless communication technologies, where the nodes that can implement perception and control in a simulated complex traffic environment have been deployed. Then, the information of the surrounding environment, including the preceding vehicles, was obtained through a wireless communication module based on the technology of vehicle to everything (V2X). Besides, the advanced model predictive control (MPC) strategy was integrated into the ICV controller with the objectives of minimizing the driving spacing and improving environmental sustainability. Finally, a co-simulation platform for ICVs based on a robot operating system (ROS) and PreScan software was constructed, and the dynamic characteristics of the controller were verified in three aspects, including car-following behaviors, fuel efficiency improvement, and carbon dioxide emission reduction. The proposed controller showed that it can reduce fuel consumption by 3.71% and reduce carbon dioxide emissions by 3.42%, in the scenarios of a preceding vehicle with constant velocity, and by 6.77% and 7.91%, respectively, in a preceding vehicle with variable velocity scenario. The demonstrated experimental results show that the proposed controller can effectively reduce fuel consumption and emissions during car-following. [ABSTRACT FROM AUTHOR]