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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]

At present, the COVID-19 pandemic still presents with outbreaks occasionally, and pedestrians in public areas are at risk of being infected by the viruses. In order to reduce the risk of cross-infection, an advanced pedestrian state sensing method for automated patrol vehicles based on multi-sensor fusion is proposed to sense pedestrian state. Firstly, the pedestrian data output by the Euclidean clustering algorithm and the YOLO V4 network are obtained, and a decision-level fusion method is adopted to improve the accuracy of pedestrian detection. Then, combined with the pedestrian detection results, we calculate the crowd density distribution based on multi-layer fusion and estimate the crowd density in the scenario according to the density distribution. In addition, once the crowd aggregates, the body temperature of the aggregated crowd is detected by a thermal infrared camera. Finally, based on the proposed method, an experiment with an automated patrol vehicle is designed to verify the accuracy and feasibility. The experimental results have shown that the mean accuracy of pedestrian detection is increased by 17.1% compared with using a single sensor. The area of crowd aggregation is divided, and the mean error of the crowd density estimation is 3.74%. The maximum error between the body temperature detection results and thermometer measurement results is less than 0.8°, and the abnormal temperature targets can be determined in the scenario, which can provide an efficient advanced pedestrian state sensing technique for the prevention and control area of an epidemic. [ABSTRACT FROM AUTHOR]

Electric vehicles have been considered into effective solutions to address energy problems in urban traffic systems for their remarkable performance in energy costs and carbon emissions reduction. However, the energy-saving effect of electric vehicles in urban traffic systems is restricted due to the complexities arising from mixed traffic conditions. To improve energy efficiency, this paper proposes an eco-driving-based mixed vehicular platoon control model for successive signalized intersections with connected and autonomous vehicles (CAVs), connected vehicles (CVs) and manned vehicles (MVs). Firstly, the dynamics model and spacing policy are analyzed, then the optimal platoon size and speed are calculated according to different traffic scenarios. Secondly, the mixed vehicular platoon control algorithm is established by considering traffic timing, energy consumption and string stability. Thirdly, an actual eco-driving-based platoon control system for tested electric vehicles is designed. The field test results demonstrate that all platoons can pass through intersections during green phase with minimum energy consumption, meanwhile the string stability can be guaranteed. It is noteworthy that the proposed model can greatly improve urban traffic capacity and energy efficiency. • A novel dynamical model of mixed vehicular platoon is explored by integrating different characteristics of CAV, CV, and MV. • An optimal speed algorithm of the leading vehicle to pass through successive signalized intersections is proposed according to signal timing, vehicular speed, and entry time. • The eco-driving-based mixed vehicular platoon control model is established, and the multi-objective function considering traffic environmental constraints are formulated to minimize the travel time and energy consumption. [ABSTRACT FROM AUTHOR]

It is agreed that connected vehicle technologies have broad implications to traffic management systems. In order to alleviate urban congestion and improve road capacity, this paper proposes a multilane spatiotemporal trajectory optimization method (MSTTOM) to reach full potential of connected vehicles by considering vehicular safety, traffic capacity, fuel efficiency, and driver comfort. In this MSTTOM, the dynamic characteristics of connected vehicles, the vehicular state vector, the optimized objective function, and the constraints are formulated. The method for solving the trajectory problem is optimized based on Pontryagin's maximum principle and reinforcement learning (RL). A typical scenario of intersection with a one-way 4-lane section is measured, and the data within 24 hours are collected for tests. The results demonstrate that the proposed method can optimize the traffic flow by enhancing vehicle fuel efficiency by 32% and reducing pollutants emissions by 17% compared with the advanced glidepath prototype application (GPPA) scheme. [ABSTRACT FROM AUTHOR]

It is agreed that connected vehicle technologies have broad implications to traffic management systems. In order to alleviate urban congestion and improve road capacity, this paper proposes a multilane spatiotemporal trajectory optimization method (MSTTOM) to reach full potential of connected vehicles by considering vehicular safety, traffic capacity, fuel efficiency, and driver comfort. In this MSTTOM, the dynamic characteristics of connected vehicles, the vehicular state vector, the optimized objective function, and the constraints are formulated. The method for solving the trajectory problem is optimized based on Pontryagin's maximum principle and reinforcement learning (RL). A typical scenario of intersection with a one-way 4-lane section is measured, and the data within 24 hours are collected for tests. The results demonstrate that the proposed method can optimize the traffic flow by enhancing vehicle fuel efficiency by 32% and reducing pollutants emissions by 17% compared with the advanced glidepath prototype application (GPPA) scheme. [ABSTRACT FROM AUTHOR]

The emerging advanced intelligent transportation systems, such as connected vehicles (CV) and cooperative vehicle-to-infrastructure system (CVIS), attract much research attentions nowadays. Taking the advantages of such systems, first, this article develops a joint control model which optimizing the speeds of the connected vehicles and coordinating signals along an arterial simultaneously. This control model forms connected vehicles into platoons so that the vehicles can pass through intersections together with no stops or the least stop time. At the same time, it optimizes signal timing plans along an arterial to achieve lower signal delay and higher throughput. Finally, the proposed model is tested on a mimic of a real-world road network in simulation. Compared with the MAXBAND model, the results indicate that the joint control model can reduce the stop time and stops of coordinate phase by up to 53.69% and 41.15%. The signalized intersection delay per vehicle is reduced by 13.19%. Meanwhile, the vehicle queue is eliminated by the platoon control and arterial signal coordination. The sensitivity analysis also shows that the signalized intersection delay reduction is proportional to the traffic flow volume. [ABSTRACT FROM AUTHOR]