Internet of Vehicles and its Applications in Autonomous Driving

2021 ◽  
IEEE Network ◽  
2019 ◽  
Vol 33 (3) ◽  
pp. 65-73 ◽  
Author(s):  
Huimin Lu ◽  
Qiang Liu ◽  
Daxin Tian ◽  
Yujie Li ◽  
Hyoungseop Kim ◽  
...  

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 98
Author(s):  
Dae-Young Kim ◽  
Minwoo Jung ◽  
Seokhoon Kim

A vehicular network is composed of an in-vehicle network (IVN) and Internet of Vehicles (IoV). IVN exchanges information among in-vehicle devices. IoV constructs Vehicle-to-X (V2X) networks outside vehicles and exchanges information among V2X elements. These days, in-vehicle devices that require high bandwidth is increased for autonomous driving services. Thus, the spread of data for vehicles is exploding. This kind of data is exchanged through IoV. Even if the Ethernet backbone of IVN carries a lot of data in the vehicle, the explosive increase in data from outside the vehicle can affect the backbone. That is, the transmission efficiency of the IVN backbone will be reduced due to excessive data traffic. In addition, when IVN data traffic is transmitted to IoV without considering IoV network conditions, the transmission efficiency of IoV is also reduced. Therefore, in this paper, we propose an IoV access gateway to controls the incoming data traffic to the IVN backbone and the outgoing data traffic to the IoV in the network environment where IVN and IoV are integrated. Computer simulations are used to evaluate the performance of the proposed system, and the proposed system shows better performance in the accumulated average transmission delay.


2021 ◽  
Author(s):  
Amal Hbaieb ◽  
Samiha AYED ◽  
Lamia CHAARI

Abstract Internet of Vehicles (IoV) is one of the attractive solutions that revolutionized automotive services. IoV is the key concept toward smart and autonomous cars. Providing different wireless connectivity’s for vehicles permits the communication inside and outside the vehicle. These connectivities allow the vehicle to interact with other vehicles and with its environment. Autonomous driving is an innovative automotive service that will be enabled by the technology advancement related to IoV and connected cars. Big data technology has a significant impact on the development of autonomous driving and IOV concept as it refers to a huge interactive networks of information. In this paper, we focus on wireless technologies and the communication system to provide Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) or Vehicle- to-Road (V2R), Vehicle-to-Sensor (V2S), Vehicle-to-Human (V2H), Vehicle-to-Cellular (V2C), Vehicle-to-Grid (V2G), and Vehicle-to-Internet (V2I) connectivities. Accordingly, this paper proposes a novel planning scheme for internet connected and autonomous driving vehicles. Particularly, we present the principal components and how they should be distributed across this kind of architecture; i.e., identifying information flows, required exchanged data and basic functionalities required to build autonomous driving service as well as the holistic hardware and software architecture involving the in-car gateway.


CICTP 2020 ◽  
2020 ◽  
Author(s):  
Kun Jiang ◽  
Yunlong Wang ◽  
Shengjie Kou ◽  
Diange Yang
Keyword(s):  

2013 ◽  
Vol 133 (9) ◽  
pp. 595-598
Author(s):  
Kenji SUZUKI ◽  
Hisaaki ISHIDA ◽  
Hirofumi INOSE ◽  
Rui KOBAYASHI
Keyword(s):  

2020 ◽  
Vol 2020 (14) ◽  
pp. 306-1-306-6
Author(s):  
Florian Schiffers ◽  
Lionel Fiske ◽  
Pablo Ruiz ◽  
Aggelos K. Katsaggelos ◽  
Oliver Cossairt

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.


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