scholarly journals Angle-Awareness Based Joint Cooperative Positioning and Warning for Intelligent Transportation Systems

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5818
Author(s):  
Zhi Dong ◽  
Bobin Yao

In future intelligent vehicle-infrastructure cooperation frameworks, accurate self-positioning is an important prerequisite for better driving environment evaluation (e.g., traffic safety and traffic efficiency). We herein describe a joint cooperative positioning and warning (JCPW) system based on angle information. In this system, we first design the sequential task allocation of cooperative positioning (CP) warning and the related frame format of the positioning packet. With the cooperation of RSUs, multiple groups of the two-dimensional angle-of-departure (AOD) are estimated and then transformed into the vehicle’s positions. Considering the system computational efficiency, a novel AOD estimation algorithm based on a truncated signal subspace is proposed, which can avoid the eigen decomposition and exhaustive spectrum searching; and a distance based weighting strategy is also utilized to fuse multiple independent estimations. Numerical simulations prove that the proposed method can be a better alternative to achieve sub-lane level positioning if considering the accuracy and computational complexity.

2004 ◽  
Author(s):  
Farid Amirouche ◽  
Khurram Mahmudi ◽  
David Zavattero

This paper addresses the issues faced by local and state governments concerning increasing traffic congestions, inadequate roadway design and traffic safety problems caused by the freight truck traffic; on the other hand, the freight industry is seeking to improve productivity by having easy access and direct routes between the intermodal facilities and the interstate highway system.


1998 ◽  

Navigation and Intelligent Transportation Systems contains 40 papers covering the technical and functional aspects of these systems including: 3D mapping, route guidance, cellular phone access, electronic compasses, and the history and future of navigation systems. The book also covers the important role of navigation in Intelligent Transportation Systems concerned with traffic management, traveler information, vehicle control systems, commercial vehicle operations, and public and rural transportation systems. The book concludes with a chapter on the Intelligent Vehicle Initiative, a joint program between the National Highway Traffic Safety Administration, the Federal Highway Administration, and the Federal Transit Administration.


2021 ◽  
Author(s):  
Abdul Saboor ◽  
Sander Coene ◽  
Evgenii Vinogradov ◽  
Emmeric Tanghe ◽  
Wout Joseph ◽  
...  

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.


2020 ◽  
Vol 10 (17) ◽  
pp. 6050
Author(s):  
Seong Kyung Kwon ◽  
Hojin Jung ◽  
Kyoung-Dae Kim

Despite recent advances in technologies for intelligent transportation systems, the safety of intersection traffic is still threatened by traffic signal violation, called the Red Light Runner (RLR). The conventional approach to ensure the intersection safety under the threat of an RLR is to extend the length of the all-red signal when an RLR is detected. Therefore, the selection of all-red signal length is an important factor for intersection safety as well as traffic efficiency. In this paper, for better safety and efficiency of intersection traffic, we propose a framework for dynamic all-red signal control that adjusts the length of all-red signal time according to the driving characteristics of the detected RLR. In this work, we define RLRs into four different classes based on the clustering results using the Dynamic Time Wrapping (DTW) and the Hierarchical Clustering Analysis (HCA). The proposed system uses a Multi-Channel Deep Convolutional Neural Network (MC-DCNN) for online detection of RLR and also classification of RLR class. For dynamic all-red signal control, the proposed system uses a multi-level regression model to estimate the necessary all-red signal extension time more accurately and hence improves the overall intersection traffic safety as well as efficiency.


Author(s):  
Juan Quintero ◽  
◽  
Alexandr Railean ◽  
Zinaida Benenson

Usage-Based Insurance (UBI) is an application of Intelligent Transportation Systems (ITS) in the context of car insurance. UBI refers to insurance models in which insurers collect driving data using a telematics device. Based on the collected information, insurers can offer individual discounts depending on driving behaviour and provide feedback about each trip. Although there are plenty of advertising materials about the benefits of UBI, its user acceptance and usability have not received much research attention so far. To cover this gap, we conducted two user studies: semi-structured interviews with UBI users and a qualitative analysis of 186 customer inquiries concerning a UBI program from a web forum of a German insurer. We found that UBI can benefit drivers, insurers and society. Moreover, the country driving conditions, the policy conditions, the users’ perceived driving style, the perception of UBI, and the premium reduction influence UBI acceptance. Regarding traffic safety, some of our participants were concerned that UBI may provoke dangerous driving behaviour under certain circumstances. Finally, we make recommendations for insurers derived from users’ views, such as to provide to drivers more control over the user interface and over the way driving feedback is given to them. Concerning the driving scores, the ways in which they are calculated should be more transparent.


Author(s):  
Zeinab E. Ahmed ◽  
Rashid A. Saeed ◽  
Amitava Mukherjee

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.


2021 ◽  
Author(s):  
Abdul Saboor ◽  
Sander Coene ◽  
Evgenii Vinogradov ◽  
Emmeric Tanghe ◽  
Wout Joseph ◽  
...  

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.


2019 ◽  
pp. 2168-2185 ◽  
Author(s):  
Zeinab E. Ahmed ◽  
Rashid A. Saeed ◽  
Amitava Mukherjee

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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