<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper analyzes the intelligent networked fleet formation mechanism under adaptive cruise control (CACC). By constructing a communication architecture system including network layer, coordination layer, control layer and physical layer, and based on environment sensing technology and 5G network, a layered control structure for autonomous driving vehicles is established. In the study, the inter-vehicle distance control strategy is proposed, and combined with the theory of metacellular automata, the study constructs an intelligent networked fleet model, which adopts the full speed difference model and adaptive cruise control vehicle following model. The simulation analysis is carried out through the actual data of a section of G6 Beijing-Tibet Expressway, and the simulation environment is a two-lane highway of 3000m, considering different CAV vehicle penetration rates (0% to 100%). The results show that with the increase of CAV penetration, the traffic flow and the congestion significantly improved. The intelligent Connected Vehicles fleet performs well in the obstacle avoidance task, effectively reducing unnecessary lane changes and shortening the congestion time compared to the conventional system, with the distance error controlled within 1m. This study shows that the intelligent connected vehicles-connected fleet has significant advantages in improving traffic flow’s operational efficiency and safety.</p>
</abstract>

This paper analyzes the intelligent networked fleet formation mechanism under adaptive cruise control (CACC). By constructing a communication architecture system including network layer, coordination layer, control layer and physical layer, and based on environment sensing technology and 5G network, a layered control structure for autonomous driving vehicles is established. In the study, the inter-vehicle distance control strategy is proposed, and combined with the theory of metacellular automata, the study constructs an intelligent networked fleet model, which adopts the full speed difference model and adaptive cruise control vehicle following model. The simulation analysis is carried out through the actual data of a section of G6 Beijing-Tibet Expressway, and the simulation environment is a two-lane highway of 3000m, considering different CAV vehicle penetration rates (0% to 100%). The results show that with the increase of CAV penetration, the traffic flow and the congestion significantly improved. The intelligent Connected Vehicles fleet performs well in the obstacle avoidance task, effectively reducing unnecessary lane changes and shortening the congestion time compared to the conventional system, with the distance error controlled within 1m. This study shows that the intelligent connected vehicles-connected fleet has significant advantages in improving traffic flow’s operational efficiency and safety.

Formation Mechanism and Analysis of Intelligent Connected Vehicles Fleet under Meta-Cellular Automata Simulation Conditions

Tianjin Sino-German University of Applied Sciences

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper analyzes the intelligent networked fleet formation mechanism under adaptive cruise control (CACC). By constructing a communication architecture...

Formation Mechanism and Analysis of Intelligent Connected Vehicles Fleet under Meta-Cellular Automata Simulation Conditions

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 15 gen 2024

Accettato: 23 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0586

Parole chiave
CACC, Environment awareness, Meta-cellular automata, Full speed difference model, Intelligent Connected Vehicles fleet

© 2024 Yu Zhao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Formation Mechanism and Analysis of Intelligent Connected Vehicles Fleet under Meta-Cellular Automata Simulation Conditions

Pubblicato online: 26 feb 2024

Pagine: -

Ricevuto: 15 gen 2024

Accettato: 23 gen 2024

DOI: https://doi.org/10.2478/amns-2024-0586

Parole chiaveCACC, Environment awareness, Meta-cellular automata, Full speed difference model, Intelligent Connected Vehicles fleet

© 2024 Yu Zhao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
CACC, Environment awareness, Meta-cellular automata, Full speed difference model, Intelligent Connected Vehicles fleet