vehicle platooning
Recently Published Documents


TOTAL DOCUMENTS

232
(FIVE YEARS 123)

H-INDEX

18
(FIVE YEARS 5)

2021 ◽  
Vol 8 (4) ◽  
pp. 407-414
Author(s):  
Eszter Puskás ◽  
Gábor Bohács

One of today's most significant challenges is sustainability, which is closely linked to environmentally friendly solutions and resource efficiency. As a solution to these goals, the concept of the Physical Internet emerged, defining the logistics network of the future as a global, open, and interconnected system. Concerning the conditions of vehicles based on Physical Internet-based systems, we cannot ignore the latest vehicle technology innovations that appear more and more intensively in parallel. The framework proposes planning at the strategic, tactical, and operational levels. Different levels of coordination implement different approaches to platoon coordination in line with the network architecture of PI-based logistics systems. We recommend the highest level of offline design in fixed π-hubs. The tactical level involves designing π-hubs online. We propose the implementation of speed-based solutions at the operational planning level.


Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3117
Author(s):  
Qingji Wen ◽  
Bin-Jie Hu

As a promising application for autonomous driving, vehicle platooning aims at increasing traffic throughput, improving road safety, and reducing air pollution and fuel consumption. However, frequent traffic perturbations will bring more fuel consumption because vehicles driving in a platoon require more control to ensure safe driving, especially in high-density scenes. In this paper, considering the traffic perturbations and high-density scenes, we integrate communication and control systems to reduce the fuel consumption of a platoon. By obtaining the velocities of multiple vehicles ahead through a long-term evolution-vehicle (LTE-V) network, we propose a modified distributed model predictive control (DMPC) method to smooth traffic perturbations and handle the constraints of vehicle state and control. In addition, considering a limited number of uplink channels that can be reused in the platoon and the uncertainty of wireless channels, a radio resource allocation optimization problem in the LTE-V network is modeled. This problem is solved in two steps including maximum vehicle-to-vehicle (V2V) broadcast distance and minimum weight matching. This resource allocation scheme increases the platoon-based V2V broadcast distance while ensuring the ergodic capacity requirement of the cellular user (CUE) uplink communication and the reliability of platoon-based V2V communication. Simulation results show that the proposed method improves fuel efficiency compared to the existing schemes.


2021 ◽  
Author(s):  

Platooning has the potential to reduce the energy consumption of commercial vehicles while improving safety; however, both advantages are currently difficult to quantify due to insufficient data and the wide range of variables affecting models. Platooning will significantly reduce the use of energy when compared to trucks driven alone, or at a safe distance for a driver without any automated assistance. Platooning will also reduce stopping distances—multiple states in the US have passed laws authorizing truck platoons to operate at shorter gaps than are authorized for normal, human-driven trucks. However, drivers typically do not currently leave the recommended gaps and, therefore, already gain much of the potential energy savings by drafting lead vehicles, albeit illegally. The automated systems associated with platooning cannot be programmed to flout safety recommendations in the way that human drivers routinely do. Therefore, actual energy savings may be minimal while safety may be greatly improved. More data will be needed to conclusively demonstrate a safety gain. Recommended safe gaps are currently highly generalized and must necessarily assume worst-case braking performance. Using a combination of condition monitoring and vehicle-to-vehicle communications, platooning systems will be able to account for the braking performance of other vehicles within the platoon. If all the vehicles in a platoon have a high level of braking performance, the platoon will be able to operate in a more efficient, tighter formation. Driver acceptance of platooning technology will increase as the systems become more effective and do not displace jobs. The increased loading of infrastructure must also be considered, and there may be requirements for upgrades on bridges or restrictions on platooning operation.


Fluids ◽  
2021 ◽  
Vol 6 (11) ◽  
pp. 404
Author(s):  
Charles Patrick Bounds ◽  
Sudhan Rajasekar ◽  
Mesbah Uddin

This paper presents a study on the flow dynamics involving vehicle interactions. In order to do so, this study first explores aerodynamic prediction capabilities of popular turbulence models used in computational fluid dynamics simulations involving tandem objects and thus, ultimately presents a framework for CFD simulations of ground vehicle platooning using a realistic vehicle model, DrivAer. Considering the availability of experimental data, the simulation methodology is first developed using a tandem arrangement of surface-mounted cubes which requires an understanding on the role of turbulence models and the impacts of the associated turbulence model closure coefficients on the prediction veracity. It was observed that the prediction accuracy of the SST k−ω turbulence model can be significantly improved through the use of a combination of modified values for the closure coefficients. Additionally, the initial validation studies reveal the inability of the Unsteady Reynolds-Averaged Navier-Stokes (URANS) approach to resolve the far wake, and its frailty in simulating tandem body interactions. The Improved Delayed Detached Eddy Simulations (IDDES) approach can resolve the wakes with a reasonable accuracy. The validated simulation methodology is then applied to the fastback DrivAer model at different longitudinal spacing. The results show that, as the longitudinal spacing is reduced, the trailing car’s drag is increased while the leading car’s drag is decreased which supports prior explanations of vortex impingement as the reason for drag changes. Additionally, unlike the case of platooning involving Ahmed bodies, the trailing model drag does not return to an isolated state value at a two car-length separation. However, the impact of the resolution of the far wake of a detailed DrivAer model, and its implication on the CFD characterization of vehicle interaction aerodynamics need further investigations.


Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7126
Author(s):  
Geonil Lee ◽  
Jae-il Jung

Cooperative driving is an essential component of intelligent transport systems (ITSs). It promises greater safety, reduced accidents, efficient traffic flow, and fuel consumption reduction. Vehicle platooning is a representative service model for ITS. The principal sub-systems of platooning systems for connected and automated vehicles (CAVs) are cooperative adaptive cruise control (CACC) systems and platoon management systems. Based on vehicle state information received through vehicle-to-vehicle (V2V) communication, the CACC system allows platoon vehicles to maintain a narrower safety distance. In addition, the platoon management system using V2V communications allows vehicles to perform platoon maneuvers reliably and accurately. In this paper, we propose a CACC system with a variable time headway and a decentralized platoon join-in-middle maneuver protocol with a trajectory planning system considering the V2V communication delay for CAVs. The platoon join-in-middle maneuver is a challenging research subject as the research must consider the requirement of a more precise management protocol and lateral control for platoon safety and string stability. These CACC systems and protocols are implemented on a simulator for a connected and automated vehicle system, PreScan, and we validated our approach using a realistic control system and V2V communication system provided by PreScan.


2021 ◽  
Vol 64 (11) ◽  
Author(s):  
Jingkai Wu ◽  
Yafei Wang ◽  
Zhaokun Shen ◽  
Lin Wang ◽  
Haiping Du ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document