A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy.

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Exponential Loss Minimization for Learning Weighted Naive Bayes Classifiers

The naive Bayesian classification method has received significant attention in the field of supervised learning. This method has an unrealistic assumption in that it views all attributes as equally important. Attribute weighting is one of the methods used to alleviate this assumption and consequently improve the performance of the naive Bayes classification. This study, with a focus on nonlinear optimization problems, proposes four attribute weighting methods by minimizing four different loss functions. The proposed loss functions belong to a family of exponential functions that makes the optimization problems more straightforward to solve, provides analytical properties of the trained classifier, and allows for the simple modification of the loss function such that the naive Bayes classifier becomes robust to noisy instances. This research begins with a typical exponential loss which is sensitive to noise and provides a series of its modifications to make naive Bayes classifiers more robust to noisy instances. Based on numerical experiments conducted using 28 datasets from the UCI machine learning repository, we confirmed that the proposed scheme successfully determines optimal attribute weights and improves the classification performance.

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Energy Optimization in Massive MIMO UAV-Aided MEC-Enabled Vehicular Networks

This paper presents a novel unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) architecture for vehicular networks. It is considered that the vehicles should complete latency-critical computation-intensive tasks either locally with on-board computation units or by offloading part of their tasks to road side units (RSUs) with collocated MEC servers. In this direction, a hovering UAV can serve as an aerial RSU (ARSU) for task processing or act as an aerial relay and further offload the computation tasks to a ground RSU (GRSU). To significantly reduce the delay during data offloading and downloading, this architecture relies on the benefits of line-of-sight (LoS) massive multiple-input–multiple-output (MIMO). Therefore, it is considered that the vehicles, the ARSU, and the GRSU employ large-scale antennas. A three-dimensional (3-D) geometrical representation of the MEC-enabled network is introduced and an optimization method is proposed that minimizes the computation-based and communication-based weighted total energy consumption (WTEC) of vehicles and ARSU subject to transmit power allocation, task allocation, and time slot scheduling. The results verify the theoretical derivations, emphasize on the effectiveness of the LoS massive MIMO transmission, and provide useful engineering insights.

*Published in the IEEE Vehicular Technology Society Section within IEEE Access.

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Design and Fabrication of Magnetic System Using Multi-Material Topology Optimization

This paper presents the design and fabrication schemes of a magnetic system consisting of segmented permanent magnet (PM) blocks, back-iron and frame structures. Here, a frame structure aims to bind PM blocks and iron structure. Non-intuitive design of segmented PMs and back-iron are obtained using multi-material topology optimization formulation. Subsequently, a non-magnetic frame structure is designed through a post-processing procedure, which is proposed using the smoothed fields of optimized PM and back-iron densities. Final design results are converted into computer-aided design (CAD) models and fabricated using conventional or additive manufacturing techniques. Segmented PM blocks, and back-iron structures are processed using water-jet cutting and wire electrical discharge machining, respectively. A frame structure is fabricated by additive manufacturing using a multi-jet printing machine. Using the proposed schemes, two magnetic systems are successfully designed and fabricated, respectively, for maximizing the magnetic field inside a rectangular cavity, and maximizing the magnetic force generated with a C-core electromagnet.

Published in the IEEE Magnetics Society Section within IEEE Access.

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Winners of the 2019 IEEE Access Best Multimedia Award (Part 2)

IEEE Access would like to congratulate the winners of the 2019 IEEE Access Best Multimedia Award (Part 2) and recipients of a $500 USD Amazon gift card for their fine contributions to IEEE Access. The full article entitled, “kNN-STUFF: kNN STreaming Unit for Fpgas” can be found by clicking here.


Winners of the 2019 IEEE Access Best Multimedia Award (Part 1)

IEEE Access would like to congratulate the winners of the 2019 IEEE Access Best Multimedia Award (Part 1) and recipients of a $500 USD Amazon gift card for their fine contributions to IEEE Access. The full article entitled, “Hidden Outlier Noise and its Mitigation” can be found by clicking here.


2018 IEEE Access Best Multimedia Award Part 2 Winners

IEEE Access would like to congratulate the winners of the 2018 IEEE Access Best Multimedia Award Part 2 and recipients of a $500 USD Amazon gift card for their fine contributions to IEEE Access. The full article entitled, “FPGA Acceleration for Computationally Efficient Symbol-Level Precoding in Multi-User Multi-Antenna Communication Systems” can be found by clicking here.


Traffic Signal Phase Scheduling Based on Device-to-Device Communication

Device-to-device (D2D) communications enable direct communications among mobile entities, which brings new revolutions to existing cellular networks. Many use cases which can benefit from D2D are introduced such as vehicles-to-vehicles communication, vehicles-to-infrastructure communication, machine-to-machine communication, and so on. With the help of these information communication techniques, we propose a real-time traffic signal control approach to relieve traffic problems in this paper. Currently, a series of traffic problems, such as traffic congestion, traffic accidents, and vehicle exhaust emission, are increasingly inconveniencing city residents, especially in rush hours. One of the most dominating approaches to relieve the traffic congest is to determine the phase timing of traffic signals. However, a major shortcoming of the existing phase timing related control strategies is of highly computational complexity, which causes, to some extent, a response delay. The approach based on D2D communication, in this paper, on one hand can collect data of various types via sensors and actuators and on the other hand can reduce the response time as much as possible. Specifically, considering an intersection with four legs, we encoded the corresponding set of signal lights of each leg using a genetic algorithm. To evaluate the efficiency of phase timing plan in this paper, we have conducted extensive simulations, and the results show that our approach can respond to the considered traffic flow within one second. Compared with other traffic signal control systems, the performance is improved almost by 67% with regards to the queue length waiting at the intersections during traffic signal light cycle(s).

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