Advances in Automated Driving Systems

Electrification, automation of vehicle control, digitalization and new mobility are the mega-trends in automotive engineering, and they are strongly connected. While many demonstrations for highly automated vehicles have been made worldwide, many challenges remain in bringing automated vehicles to the market for private and commercial use. The main challenges are as follows: reliable machine perception; accepted standards for vehicle-type approval and homologation; verification and validation of the functional safety, especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human-controlled vehicles in mixed traffic; human–machine interaction and usability; manipulation, misuse and cyber-security; the system costs of hard- and software and development efforts. This Special Issue was prepared in the years 2021 and 2022 and includes 15 papers with original research related to recent advances in the aforementioned challenges. The topics of this Special Issue cover: Machine perception for SAE L3+ driving automation; Trajectory planning and decision-making in complex traffic situations; X-by-Wire system components; Verification and validation of SAE L3+ systems; Misuse, manipulation and cybersecurity; Human–machine interactions, driver monitoring and driver-intention recognition; Road infrastructure measures for the introduction of SAE L3+ systems; Solutions for interactions between human- and machine-controlled vehicles in mixed traffic

Cyber-Physical Embedded Systems with Transient Supervisory Command and Control: A Framework for Validating Safety Response in Automated Collision Avoidance Systems

The ability to design and engineer complex and dynamical Cyber-Physical Systems (CPS) requires a systematic view that requires a definition of level of automation intent for the system. Since CPS covers a diverse range of systemized implementations of smart and intelligent technologies networked within a system of systems (SoS), the terms “smart” and “intelligent” is frequently used in describing systems that perform complex operations with a reduced need of a human-agent. The difference between this research and most papers in publication on CPS is that most other research focuses on the performance of the CPS rather than on the correctness of its design. However, by using both human and machine agency at different levels of automation, or autonomy, the levels of automation have profound implications and affects to the reliability and safety of the CPS. The human-agent and the machine-agent are in a tidal lock of decision-making using both feedforward and feedback information flows in similar processes, where a transient shift within the level of automation when the CPS is operating can have undesired consequences. As CPS systems become more common, and higher levels of autonomy are embedded within them, the relationship between human-agent and machine-agent also becomes more complex, and the testing methodologies for verification and validation of performance and correctness also become more complex and less clear. A framework then is developed to help the practitioner to understand the difficulties and pitfalls of CPS designs and provides guidance to test engineering design of soft computational systems using combinations of modeling, simulation, and prototyping

Journal of Telecommunications and Information Technology

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The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

The impact of visibility range and atmospheric turbulence on free space optical link performance in South Africa.

Doctoral Degree. University of KwaZulu-Natal, Durban.In the recent years, the development of 5G and Massive Internet of Things (MIoT) technologies are fast increasing regularly. The high demand for a back-up and complimentary link to the existing conventional transmission systems (such as RF technology) especially for the “last-mile” phenomenon has increased significantly. Therefore, this has brought about a persistent requirement for a better and free spectrum availability with a higher data transfer rate and larger bandwidth, such as Free Space Optics (FSO) technology using very high frequency (194 −545 ) transmission system. There is currently unavailable comprehensive information that would enable the design of FSO networks for various regions of South Africa based on the impact of certain weather parameters such as visibility range (mainly in terms of fog and haze) and atmospheric turbulence (in terms of Refractive Index Structure Parameter (RISP)) on FSO link performance. The components of the first part of this work include Visibility Range Distribution (VRD) modeling using suitable probability density function (PDF) models, and prediction of the expected optical attenuation due to scattering and its cumulative distribution and modeling. The VRD modelling performed in this work, proposed various location-based PDF models, and it was suggested that the Generalized Pareto distribution model best suited the distributions of visibility in all the cities. The result of this work showed that the optical attenuation due to scattering within the coastal and near-coastal areas could reach as high as 169 / or more, while in the non-coastal areas it varies between 34 / and 169 /, which suggests significant atmospheric effects on the FSO link, mostly during the winter period. The BER performance analysis was performed and suitable mitigating techniques (such as 4 × 4 MIMO with BPSK and L-PPM schemes) were suggested in this work. The general two-term exponential distribution model provided a good fit to the cumulative distribution of the atmospheric attenuation due to scattering for all the locations. In order to ascertain how atmospheric variables contribute or affect the visibility range, which in turn determines the level of attenuation due to scattering, a time series prediction of visibility using Artificial Neural Network (ANN) technique was investigated, where an average reliability of about 83 % was achieved for all the stations considered. This suggests that climatic parameters highly correlate to visibility when they are all combined together, and this gave significant predictions which will enable FSO officials to develop and maintain a strategic plan for the future years. The modules of the second part of this work encompass the determination of the Atmospheric Turbulence Level (ATL) for each of the locations in terms of RISP (2) and its equivalent scintillation index, and then the estimation of the optical attenuation due to scintillation. The cumulative distributions of the optical attenuation due to scintillation and its modeling were also carried out. This research work has been able to achieve the prediction of the ground turbulence strength (through the US-Army Research Laboratory (US-ARL) Model) in terms of RISP using climatic data. In an attempt to provide a more reliable study into the atmospheric turbulence strength within South Africa, this work explores the characteristic behavior of several meteorological variables and other thermodynamic properties such as inner and outer characteristic scales, Monin-Obhukov length, potential temperature gradient, bulk wind shear and so on. According to the predicted RISP from meteorological variables (such as temperature, relative humidity, pressure, wind speed, water vapour, and altitude), location-based and general attenuation due to scintillation models were developed for South Africa to estimate the optical attenuation. The attenuation due to scintillation results show that the summer and autumn seasons have higher ATL, where January, February and December have the highest mean RISP across all the locations under study. Also, the comparison of the monthly averages of the estimated attenuations revealed that at 850 nm more atmospheric turbulence with specific attenuations between 21.04 / and 24.45 / were observed in the coastal and near-coastal areas than in the non-coastal areas. The study proposes the two-term Sum of Sine distribution model for the cumulative distribution of the optical attenuation based on scintillation, which should be adopted for South Africa. The obtained results in this work for the contributions of scattering and turbulence to the optical link, and the design of the link budget will serve as the major criteria parameters to further compare the outcomes of these results with that of the available terrestrial FSO systems and other conventional transmission systems like RF systems

Future Transportation

Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others

Internet of Things and Neural Network Based Energy Optimization and Predictive Maintenance Techniques in Heterogeneous Data Centers

Rapid growth of cloud-based systems is accelerating growth of data centers. Private and public cloud service providers are increasingly deploying data centers all around the world. The need for edge locations by cloud computing providers has created large demand for leasing space and power from midsize data centers in smaller cities. Midsize data centers are typically modular and heterogeneous demanding 100% availability along with high service level agreements. Data centers are recognized as an increasingly troublesome percentage of electricity consumption. Growing energy costs and environmental responsibility have placed the data center industry, particularly midsize data centers under increasing pressure to improve its operational efficiency. The power consumption is mainly due to servers and networking devices on computing side and cooling systems on the facility side. The facility side systems have complex interactions with each other. The static control logic and high number of configuration and nonlinear interdependency create challenges in understanding and optimizing energy efficiency. Doing analytical or experimental approach to determine optimum configuration is very challenging however, a learning based approach has proven to be effective for optimizing complex operations. Machine learning methodologies have proven to be effective for optimizing complex systems. In this thesis, we utilize a learning engine that learns from operationally collected data to accurately predict Power Usage Effectiveness (PUE) and creation of intelligent method to validate and test results. We explore new techniques on how to design and implement Internet of Things (IoT) platform to collect, store and analyze data. First, we study using machine learning framework to predictively detect issues in facility side systems in a modular midsize data center. We propose ways to recognize gaps between optimal values and operational values to identify potential issues. Second, we study using machine learning techniques to optimize power usage in facility side systems in a modular midsize data center. We have experimented with neural network controllers to further optimize the data suite cooling system energy consumption in real time. We designed, implemented, and deployed an Internet of Things framework to collect relevant information from facility side infrastructure. We designed flexible configuration controllers to connect all facility side infrastructure within data center ecosystem. We addressed resiliency by creating reductant controls network and mission critical alerting via edge device. The data collected was also used to enhance service processes that improved operational service level metrics. We observed high impact on service metrics with faster response time (increased 77%) and first time resolution went up by 32%. Further, our experimental results show that we can predictively identify issues in the cooling systems. And, the anomalies in the systems can be identified 30 days to 60 days ahead. We also see the potential to optimize power usage efficiency in the range of 3% to 6%. In the future, more samples of issues and corrective actions can be analyzed to create practical implementation of neural network based controller for real-time optimization.Ph.D.Information Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136074/1/Final Dissertation Vishal Singh.pdfDescription of Final Dissertation Vishal Singh.pdf : Dissertatio