On-Road Trajectory Planning of Connected and Automated Vehicles in Complex Traffic Settings: A Hierarchical Framework of Trajectory Refinement

This paper presents a hierarchical framework for on-road trajectory planning in complex traffic environments. Firstly, the processing of sparse coarse trajectories involves the utilization of DP (Dynamic Programming) generation and interpolation techniques. Then, for the waypoints with collision risk in the smoothed trajectory, the spiral search method is used to find some safe alternate waypoints. The alternate waypoints and the previous ones without collision risk form the amended trajectory. Concurrently, safety tunnels are constructed along the amended trajectory for the ego vehicle. Furthermore, with the constraint conditions of vehicle kinematics model and safety tunnels, nonlinear program (NLP) optimization is carried out for the amended trajectory of ego vehicle to obtain smooth and safe trajectories. For typical cases, simulation experiments demonstrate that the ego vehicle can ensure collision safety in dynamic traffic scenarios, while maintaining smooth vehicle velocity and small jitter of the front wheel angle. The proposed trajectory planning framework provides a novel decision-making method for connected and automated vehicles (CAVs).

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AMS Circuit Design Optimization Technique Based on ANN Regression Model With VAE Structure

The advanced design of an analog mixed-signal circuit is not simple enough to meet the requirements of the performance matrix as well as robust operations under process-voltage-temperature (PVT) changes. Even commercial products demand stringent specifications while maintaining the system’s performance. The main objectives of this study are to increase the efficiency of the design optimization process by configuring the design process in multiple regression modeling stages, to characterize our target circuit into a regression model including PVT variations, and to enable a search for co- optimum design points while simultaneously checking performance sensitivity. We used an artificial neural network (ANN) to develop a regression model and divided the ANN modeling process into coarse and fine simulation steps. In addition, we applied a variational autoencoder (VAE) structure to the ANN model to reduce the training error due to an insufficient input sample. According to the proposed algorithm, the AMS circuit designer can quickly search for the co- optimum point, which results in the best performance, while the least sensitive operation as the design process uses a regression model instead of launching heavy SPICE simulations. In this study, a voltage-controlled oscillator (VCO) is selected to prove the proposed algorithm. Under various design conditions (CMOS 180 nm, 65 nm, and 45 nm processes), we proceed with the proposed design flow to obtain the best performance score that can be evaluated by a figure-of-merit (FoM). As a result, the proposed regression model-based design flow achieves twice accurate results in comparison to that of the conventional single-step design flow.

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On the Cyber-Physical Needs of DER-Based Voltage Control/Optimization Algorithms in Active Distribution Network

With the increasing penetration of distributed energy resources (DERs) and extensive usage of information and communications technology (ICT) in decision-making, mechanisms to control/optimize transmission and distribution grid voltage would experience a paradigm shift. Given the introduction of inverter-based DERs with vastly different dynamics, real-world performance characterization of the cyber-physical system (CPS) in terms of dynamical performance, scalability, robustness, and resiliency with the new control algorithms require precise algorithmic classification and suitable metrics. It has been identified that classical controller definitions along with three inter-disciplinary domains, such as (i) power system, (ii) optimization, control, and decision-making, and (iii) networking and cyber-security, would provide a systematic basis for the development of an extended metric for algorithmic performance evaluation; while providing the taxonomy. Furthermore, a majority of these control algorithms operate in multiple time scales, and therefore, algorithmic time decomposition facilitates a new way of performance analysis. Extended discussion on communication requirements while focusing on the architectural subtleties of algorithms is expected to identify the real-world deployment challenges of voltage control/optimization algorithms in the presence of cyber vulnerabilities and associated mitigation mechanisms affecting the controller performance with DERs. Finally, the detailed discussion provided in this paper identifies the modeling requirements of the CPS for real-world deployment, specific to voltage control, facilitating the development of a unified test-bed.

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Published in the IEEE Power & Energy Society Section within IEEE Access

DNN Partitioning for Inference Throughput Acceleration at the Edge

Deep neural network (DNN) inference on streaming data requires computing resources to satisfy inference throughput requirements. However, latency and privacy sensitive deep learning applications cannot afford to offload computation to remote clouds because of the implied transmission cost and lack of trust in third-party cloud providers. Among solutions to increase performance while keeping computation on a constrained environment, hardware acceleration can be onerous, and model optimization requires extensive design efforts while hindering accuracy. DNN partitioning is a third complementary approach, and consists of distributing the inference workload over several available edge devices, taking into account the edge network properties and the DNN structure, with the objective of maximizing the inference throughput (number of inferences per second). This paper introduces a method to predict inference and transmission latencies for multi-threaded distributed DNN deployments, and defines an optimization process to maximize the inference throughput. A branch and bound solver is then presented and analyzed to quantify the achieved performance and complexity. This analysis has led to the definition of the acceleration region, which describes deterministic conditions on the DNN and network properties under which DNN partitioning is beneficial. Finally, experimental results confirm the simulations and show inference throughput improvements in sample edge deployments.

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Reducing Losses and Energy Storage Requirements of Modular Multilevel Converters With Optimal Harmonic Injection

Due to the single phase characteristic of the individual arms of the Modular Multilevel Converter (MMC) topology, the difference between the instantaneous AC and DC side power must be buffered in the module capacitors. This results in large module capacitors compromising the power density and cost of the MMC. In this paper, a multi-objective optimization scheme is formulated that aims at reducing the required module capacitance and the semiconductor losses at the same time. Further attention is paid to the maximum AC voltage amplitude or the maximum current. The optimization scheme is based on the injection of circulating current and AC common mode voltage harmonics. Unlike most existing optimization schemes it considers the actual trajectories of capacitor voltage and arm output voltage to maximize the savings in module capacitance and semiconductor losses. For an exemplary medium voltage MMC parameter set, capacitance value reductions of more than 50% are achieved while the semiconductor losses decrease by 8- 18%. Based on a volume estimation for MMC modules, this results a volume reduction of up to 45%.

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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.