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Managing unsignalized intersections is further developed in the context of automated driving with vehicle-road coordination. In this context, the virtual platoon of lining vehicles into a one-dimensional virtual queue is based on a fully automated driving environment. It cannot be used in today's mixed traffic, and improper sequencing rules can cause significant delays. Thus, we propose a hierarchical framework to manage unsignalized intersections, where the lower-level distributed controller controls the mixed vehicles to form a vehicle platoon with CAV at the head of the platoon. The upper-level collaborative controller determines the passing order of the vehicle platoon. In deciding the passing order, we propose the Mixed Platoon Scheduling Model (MPSM) to improve the traffic safety and efficiency of unsignalized intersections in mixed traffic environments and to obtain the optimal vehicle passing order without collisions. First, MPSM transforms the conflict scheduling optimization problem into a graphical optimization problem to get a Mixed conflict-directed graph (MCDG) of nodes. Second, the spanning tree's depth and average width are optimized by coexisting undirected graphs so that the number of vehicle platoons passing through the intersection simultaneously increases. Then, we change the order of the spanning tree nodes to reduce potential delay. The effectiveness of the intersection management framework was evaluated experimentally. The results show that MPSM possesses good delay performance and has an advantage over Adaptive Signal Control Method (Adaptive-SIM), FCFS, and DFST algorithms, which can be applied in different traffic environments.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12284081 | PMC |
http://dx.doi.org/10.1038/s41598-025-11779-0 | DOI Listing |
Commun Eng
August 2025
School of Systems Science, Beijing Jiaotong University, Beijing, China.
Addressing urban congestion through enhanced traffic capacity has emerged as a critical objective for connected autonomous driving technologies. An irredundant communication connectivity topology is essential for ensuring the high efficiency and stability of the traffic system, which has not been fully validated due to the scarcity of real-world tests. Motivated by this fact, this paper deploys a connected autonomous vehicle platoon without relying on the information of a platoon leader to preserve the possibility of extending the platoon in future practical applications.
View Article and Find Full Text PDFNat Commun
August 2025
School of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, P. R. China.
As autonomous vehicles and traditional vehicles will coexist for several decades, how to efficiently manage the mixed traffic, while enhancing road throughput, fuel consumption and traffic stability becomes a challenge. This is due to the randomness and heterogeneity of traditional vehicles interspersed among autonomous vehicles. Moreover, communication delays arising from the shared wireless communication network substantially degrade the performance of platooning control for connected autonomous vehicles.
View Article and Find Full Text PDFPLoS One
August 2025
Hangzhou Vocational and Technical College, Hangzhou, Zhejiang, China.
Connected and automated vehicle (CAV) platooning, enabled by Vehicle-to-Vehicle (V2V) communication, promises significant improvements in traffic safety, throughput, and energy efficiency. However, communication constraints - such as range limitations and intermittent connectivity - disrupt information flow, destabilizing platoon dynamics. Existing models lack a unified framework to analyze how these constraints propagate through CAV interactions.
View Article and Find Full Text PDFSci Rep
July 2025
Traffic Engineering Group, Institute for Transport Planning and Systems, ETH Zurich, Stefano-Franscini-Platz 5, 8093, Zurich, Switzerland.
Vehicle trajectories offer valuable insights for a wide range of road transportation applications. Due to the rise of drone technology, a growing branch of literature explores optical vehicle trajectory extraction from aerial videos, where object detection using neural networks is an important component. Horizontal bounding box object detection struggles to differentiate well between rotated vehicles, especially when dealing with complex backgrounds or densely packed vehicles.
View Article and Find Full Text PDFSci Rep
July 2025
School of Transportation, Southeast University, Nanjing, 211189, China.
Managing unsignalized intersections is further developed in the context of automated driving with vehicle-road coordination. In this context, the virtual platoon of lining vehicles into a one-dimensional virtual queue is based on a fully automated driving environment. It cannot be used in today's mixed traffic, and improper sequencing rules can cause significant delays.
View Article and Find Full Text PDF