scholarly journals Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities

2021 ◽  
Vol 1 (2) ◽  
pp. 326-350
Author(s):  
Anunay Gupta ◽  
Tanzina Afrin ◽  
Evan Scully ◽  
Nita Yodo
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Significant Role ◽  
Intelligent Transportation Systems ◽  
State Of The Art ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
The State ◽  
Research Areas ◽  
Aerial Vehicles ◽  
Challenges And Opportunities

The adoption of Unmanned Aerial Vehicles (UAVs) in numerous sectors is projected to grow exponentially in the future as technology advances and regulation evolves. One of the promising applications of UAVs is in transportation systems. As the current transportation system is moving towards Intelligent Transportation Systems (ITS), UAVs will play a significant role in the functioning of ITS. This paper presents a survey on the recent advances of UAVs and their roles in current and future transportation systems. Moreover, the emerging technologies of UAVs in the transportation section and the current research areas are summarized. From the discussion, the challenges and opportunities of integrating UAVs towards future ITS are highlighted. In addition, some of the potential research areas involving UAVs in future ITS are also identified. This study aims to lay a foundation for the development of future intelligent and resilient transportation systems.

scholarly journals Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1136
Author(s):  
David Augusto Ribeiro ◽  
Juan Casavílca Silva ◽  
Renata Lopes Rosa ◽  
Muhammad Saadi ◽  
Shahid Mumtaz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Intelligent Transportation Systems ◽  
Light Field ◽  
State Of The Art ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Machine Learning Algorithms ◽  
Learning Framework ◽  
Reconstruction Methods

Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time.

scholarly journals An Improved Stacked Denoise Autoencoder with Elu Activation Function for Traffic Data Imputation

2019 ◽  
Vol 8 (11) ◽  
pp. 3951-3954
Keyword(s):  
Deep Learning ◽  
Missing Data ◽  
Intelligent Transportation Systems ◽  
State Of The Art ◽  
Intelligent Transportation ◽  
Activation Function ◽  
Transportation Systems ◽  
Data Imputation ◽  
Traffic Data ◽  
Spatio Temporal

Traffic data plays a major role in transport related applications. The problem of missing data has greatly impact the performance of Intelligent transportation systems(ITS). In this work impute the missing traffic data with spatio-temporal exploitation for high precision result under various missing rates. Deep learning based stacked denoise autoencoder is proposed with efficient Elu activation function to remove noise and impute the missing value.This imputed value will be used in analyses and prediction of vehicle traffic. Results are discussed that the proposed method outperforms well in state of the art approaches.

scholarly journals Elevating the future of mobility UAV-enabled Intelligent Transportation Systems

2021 ◽  
Author(s):  
Abdul Saboor ◽  
Sander Coene ◽  
Evgenii Vinogradov ◽  
Emmeric Tanghe ◽  
Wout Joseph ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Base Stations ◽  
Unmanned Aerial Systems ◽  
Traffic Efficiency ◽  
Autonomous Operation ◽  
Aerial Vehicles ◽  
The Future

Intelligent Transportation Systems (ITS) improve traffic efficiency, traffic management, driver’s comfort, and safety. They consist of a broad range of components, including vehicles, sensors, Base Stations, Road Side Units, and road infrastructure (i.e., traffic signals). ITS of the near future will need to support multi-modal transportation schemes (including ground and aerial vehicles, so-called Urban Air Mobility). ITS will have to be integrated with Unmanned Aerial Systems Traffic Management (UTM) and rely on 3 Dimensional (3D) connectivity provided by Integrated Aerial-Terrestrial 6G networks to achieve this support. In other words, various types of Unmanned Aerial Vehicles (UAVs) will become integral parts of future ITS due to their mobility, autonomous operation, and communication/processing capabilities. This article presents our view on the future integration of ITS and UTM systems, enabling wireless technologies and open research questions. We also present how UAVs can be used to enhance the performance of the currently available ITS.

Challenges and Opportunities in Vehicular Cloud Computing

2018 ◽  
pp. 57-74 ◽  
Author(s):  
Zeinab E. Ahmed ◽  
Rashid A. Saeed ◽  
Amitava Mukherjee
Keyword(s):  
Cloud Computing ◽  
Intelligent Transportation Systems ◽  
Vehicular Ad Hoc Networks ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud ◽  
Traffic Efficiency ◽  
Challenges And Opportunities ◽  
Hoc Networks

Vehicular ad-hoc networks (VANET) have become an important research area due to their ability to allow sharing resources among the users to carry out their application and provide services of transport and traffic management. VANET communication allows exchange of sensitive information among nearby vehicles such as condition of weather and road accidents in order to improve vehicle traffic efficiency through Intelligent Transportation Systems (ITS). Many technologies have been developed to enhance ITS. Recently, vehicular cloud computing (VCC) has been developed in order to overcome the drawbacks VANET. VCC technology provides low-cost services to vehicles and capable of managing road traffic efficiently by using the vehicular sources (such as internet) to make decisions and for storage. VCC is considered as the basis for improving and developing intelligent transportation systems. It plays a major role in people's lives due to its safety, security, trust, and comfort to passengers and drivers. This chapter investigates the vehicular cloud computing. The authors first concentrate on architectures. Then, they highlight applications and features provided by VCC. Additionally, they explain the challenges for VCC. Finally, the authors present opportunities and future for VCC.

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