Towards Automotive Embedded Systems with Self-X Properties

With self-adaptation and self-organization new paradigms for the management of distributed systems have been introduced. By enhancing the automotive software system with self-X capabilities, e.g. self-healing, self-configuration and self-optimization, the complexity is handled while increasing the flexibility, scalability and dependability of these systems. In this chapter we present an approach for enhancing automotive systems with self-X properties. At first, we discuss the benefits of providing automotive software systems with self-management capabilities and outline concrete use cases. Afterwards, we will discuss requirements and challenges for realizing adaptive automotive embedded systems

A Testing and Experimenting Environment for Microscopic Traffic Simulation Utilizing Virtual Reality and Augmented Reality

Microscopic traffic simulation (MTS) is the emulation of real-world traffic movements in a virtual environment with various traffic entities. Typically, the movements of the vehicles in MTS follow some predefined algorithms, e.g., car-following models, lane changing models, etc. Moreover, existing MTS models only provide a limited capability of two- and/or three-dimensional displays that often restrict the user’s viewpoint to a flat screen. Their downscaled scenes neither provide a realistic representation of the environment nor allow different users to simultaneously experience or interact with the simulation model from different perspectives. These limitations neither allow the traffic engineers to effectively disseminate their ideas to various stakeholders of different backgrounds nor allow the analysts to have realistic data about the vehicle or pedestrian movements. This dissertation intends to alleviate those issues by creating a framework and a prototype for a testing environment where MTS can have inputs from user-controlled vehicles and pedestrians to improve their traffic entity movement algorithms as well as have an immersive M3 (multi-mode, multi-perspective, multi-user) visualization of the simulation using Virtual Reality (VR) and Augmented Reality (AR) technologies. VR environments are created using highly realistic 3D models and environments. With modern game engines and hardware available on the market, these VR applications can provide a highly realistic and immersive experience for a user. Different experiments performed by real users in this study prove that utilizing VR technology for different traffic related experiments generated much more favorable results than the traditional displays. Moreover, using AR technologies for pedestrian studies is a novel approach that allows a user to walk in the real world and the simulation world at a one-to-one scale. This capability opens a whole new avenue of user experiment possibilities. On top of that, the in-environment communication chat system will allow researchers to perform different Advanced Driver Assistance System (ADAS) studies without ever needing to leave the simulation environment. Last but not least, the distributed nature of the framework enables users to participate from different geographic locations with their choice of display device (desktop, smartphone, VR, or AR). The prototype developed for this dissertation is readily available on a test webpage, and a user can easily download the prototype application without needing to install anything. The user also can run the remote MTS server and then connect their client application to the server

Modeling of On-line Traffic Control and Management Network for Operational and Communication Performance Evaluation

Communication systems are the backbone of every effective and reliable traffic control and management application. While traditional fiber optics and telephone communications have long been used in managing and controlling highway traffic, wireless communication technology shows great promise as an alternative solution in traffic management applications due to their suitability for deployment in rural areas, and their flexibility and cost-effectiveness for system expansion. However, the detailed characteristics of various wireless communication technologies and real performance in the field have not been systematically studied. To augment this existing knowledge so that traffic professionals may better utilize these technologies to improve traffic safety, mobility and efficiency, this study aims to 1) identify existing wireless communication technologies used in ITS, and potential wireless communication alternatives that can be widely used in ITS, 2) evaluate the performance, cost and reliability of existing and potential wireless communication technologies in supporting on-line traffic control and management functions, and 3) apply benefit-cost analysis to identify the impacts of using these wireless technologies to support on-line traffic management. To achieve these research objectives, the author first conducted an interview to discover the specifications of existing communication infrastructures deployed for various ITS related applications and the usage of wireless technologies in different states. Moreover, the author proposed a network design process that considered wireless coverage range and network topology, followed with case studies utilizing Wireless Fidelity (WiFi) and Worldwide Interoperability for Microwave Access (WiMAX) technologies to support a traffic surveillance system in seven metropolitan areas throughout South Carolina. Field tests were conducted to evaluate the performance and reliability of wireless transmissions between adjacent sensor nodes. After that, the author applied a communication simulator, ns-2, to compare the communication performance of a traffic sensor network with WiFi and WiMAX technologies under infrastructure and mesh topologies, and environmental conditions. Based on these simulation results, the author conducted performance-cost analysis for these selected technologies and topologies. The WiFi field test results indicated that wireless communication performance between two traffic sensors significantly degrades after 300 ft; this distance, however, may vary with the modulation rates and transmission power upon which the system operates. WiMAX nomadic test suggested that line-of-sight (LOS) greatly affects the connectivity level. Moreover, the capabilities and the performance of the WiMAX network are sometimes affected by the characteristics of the client radio. The simulation analysis and benefit-cost analysis indicated a WiFi mesh network solution has the highest throughput-cost ratio, 109 bits/dollar for supporting traffic surveillance systems, while the WiMAX infrastructure option provides the greatest amount of excess bandwidth, 9.15Mbps per device, which benefits the system\u27s future expansion. This dissertation provides an important foundation for further investigation of the performance and reliability of different wireless technologies. In addition, research results presented in this dissertation will benefit transportation agencies and other stakeholders in evaluating and selecting wireless communication options for different traffic control and management applications

Quantifying non-exhaust emissions and the impact of hybrid and electric vehicles using combined measurement and modelling approaches

Road traffic is a significant emission source of urban particulate matter (PM). Due to the implementation of exhaust regulatory standards in the UK, PM emissions which arise from the wear of brakes, tyres and the road surface, together with the resuspension of road dust are now predicted to exceed tailpipe emissions. While a growing body of academic literature has developed in recent years, non-exhaust emissions (NEE) remain unregulated and largely understudied, and the impact of powertrain electrification on the vehicle fleet has not been quantified. Thus, the aim of this thesis is to improve our understanding of these important emission sources and to determine the impact of NEE on urban air pollution - both now, and in the future. A series of highly time-resolved atmospheric measurement campaigns has been undertaken at roadside and background locations to determine roadside traffic increments. These measurements provide a comprehensive dataset of traffic emissions in London, Birmingham and Manchester, incorporating locations with different vehicle mix and speed, during summer and winter periods. PM mass and elemental tracers have been used to estimate the contribution of NEE concentrations using a scaling factor approach. A novel CO2 dilution approach has been undertaken to determine average fleet emission factors (EFs), whilst the impact of electric vehicle regenerative braking has also been simulated. The results indicate that NEE concentrations and EFs are highly dependent upon meteorological conditions, traffic speed, traffic volume and vehicle class. Brake wear is the dominant source of road traffic PM emissions in congested environments, whilst for each emission source, heavy duty vehicles (HDVs) contribute an order of magnitude greater than light duty vehicles (LDVs). On the other hand, despite the predicted increase in mass, the regenerative braking simulations suggest that passenger vehicles under electric powertrains will reduce brake wear emissions by 65 – 95%. This reduction depends on the assessed drive cycle and vehicle class, highlighting the importance of driving style on future brake wear emissions. The EFs developed in this thesis have been combined with traffic forecasts to project total national emissions in the UK up to 2035 – and can be used to validate the national atmospheric emission inventory. To conclude, a number of recommendations have been made with respect to air quality measurement strategies and emission policies which are needed to further our understanding of NEE, and to reduce these traffic-related emissions. It is proposed that a multi-disciplinary study should be undertaken encompassing laboratory dynamometer testing, on-vehicle measurements and environmental atmospheric measurements.Open Acces

Digital document imaging systems: An overview and guide

This is an aid to NASA managers in planning the selection of a Digital Document Imaging System (DDIS) as a possible solution for document information processing and storage. Intended to serve as a manager's guide, this document contains basic information on digital imaging systems, technology, equipment standards, issues of interoperability and interconnectivity, and issues related to selecting appropriate imaging equipment based upon well defined needs

CPA\u27s guide to information security

https://egrove.olemiss.edu/aicpa_guides/1963/thumbnail.jp

WEIGH-IN-MOTION DATA-DRIVEN PAVEMENT PERFORMANCE PREDICTION MODELS

The effective functioning of pavements as a critical component of the transportation system necessitates the implementation of ongoing maintenance programs to safeguard this significant and valuable infrastructure and guarantee its optimal performance. The maintenance, rehabilitation, and reconstruction (MRR) program of the pavement structure is dependent on a multidimensional decision-making process, which considers the existing pavement structural condition and the anticipated future performance. Pavement Performance Prediction Models (PPPMs) have become indispensable tools for the efficient implementation of the MRR program and the minimization of associated costs by providing precise predictions of distress and roughness based on inventory and monitoring data concerning the pavement structure\u27s state, traffic load, and climatic conditions. The integration of PPPMs has become a vital component of Pavement Management Systems (PMSs), facilitating the optimization, prioritization, scheduling, and selection of maintenance strategies. Researchers have developed several PPPMs with differing objectives, and each PPPM has demonstrated distinct strengths and weaknesses regarding its applicability, implementation process, and data requirements for development. Traditional statistical models, such as linear regression, are inadequate in handling complex nonlinear relationships between variables and often generate less precise results. Machine Learning (ML)-based models have become increasingly popular due to their ability to manage vast amounts of data and identify meaningful relationships between them to generate informative insights for better predictions. To create ML models for pavement performance prediction, it is necessary to gather a significant amount of historical data on pavement and traffic loading conditions. The Long-Term Pavement Performance Program (LTPP) initiated by the Federal Highway Administration (FHWA) offers a comprehensive repository of data on the environment, traffic, inventory, monitoring, maintenance, and rehabilitation works that can be utilized to develop PPPMs. The LTPP also includes Weigh-In-Motion (WIM) data that provides information on traffic, such as truck traffic, total traffic, directional distribution, and the number of different axle types of vehicles. High-quality traffic loading data can play an essential role in improving the performance of PPPMs, as the Mechanistic-Empirical Pavement Design Guide (MEPDG) considers vehicle types and axle load characteristics to be critical inputs for pavement design. The collection of high-quality traffic loading data has been a challenge in developing Pavement Performance Prediction Models (PPPMs). The Weigh-In-Motion (WIM) system, which comprises WIM scales, has emerged as an innovative solution to address this issue. By leveraging computer vision and machine learning techniques, WIM systems can collect accurate data on vehicle type and axle load characteristics, which are critical factors affecting the performance of flexible pavements. Excessive dynamic loading caused by heavy vehicles can result in the early disintegration of the pavement structure. The Long-Term Pavement Performance Program (LTPP) provides an extensive repository of WIM data that can be utilized to develop accurate PPPMs for predicting pavement future behavior and tolerance. The incorporation of comprehensive WIM data collected from LTPP has the potential to significantly improve the accuracy and effectiveness of PPPMs. To develop artificial neural network (ANN) based pavement performance prediction models (PPPMs) for seven distinct performance indicators, including IRI, longitudinal crack, transverse crack, fatigue crack, potholes, polished aggregate, and patch failure, a total of 300 pavement sections with WIM data were selected from the United States of America. Data collection spanned 20 years, from 2001 to 2020, and included information on pavement age, material properties, climatic properties, structural properties, and traffic-related characteristics. The primary dataset was then divided into two distinct subsets: one which included WIMgenerated traffic data and another which excluded WIM-generated traffic data. Data cleaning and normalization were meticulously performed using the Z-score normalization method. Each subset was further divided into two separate groups: the first containing 15 years of data for model training and the latter containing 5 years of data for testing purposes. Principal Component Analysis (PCA) was then employed to reduce the number of input variables for the model. Based on a cumulative Proportion of Variation (PoV) of 96%, 12 input variables were selected. Subsequently, a single hidden layer ANN model with 12 neurons was generated for each performance indicator. The study\u27s results indicate that incorporating Weigh-In-Motion (WIM)-generated traffic loading data can significantly enhance the accuracy and efficacy of pavement performance prediction models (PPPMs). This improvement further supports the suitability of optimized pavement maintenance scheduling with minimal costs, while also ensuring timely repairs to promote acceptable serviceability and structural stability of the pavement. The contributions of this research are twofold: first, it provides an enhanced understanding of the positive impacts that high-quality traffic loading data has on pavement conditions; and second, it explores potential applications of WIM data within the Pavement Management System (PMS)

Development of an Improved System for Contract Time Determination

Contract time, is the maximum time allowed for completion of all work described in contract documents. The determination of contract time affects not only the actual duration of the construction project, but also such aspects of construction such as costs, resource planning, selection of contractors and traffic problems. An accurate estimation of contract time reduces the impact of a delayed project on the local economy and provides justification to contractors during construction claims. This research performed an extensive literature review on various contract time determination procedures and systems developed and used by various state agencies to estimate contract time for their highway projects. This study surveyed 24 DOTs in the United States to determine the prevalent contract time procedures and determined their advantages and disadvantages. Oklahoma Contract Time Determination System (Ok-CTDS) is a contract time estimating system for Tier-II type highway projects of ODOT which are categorized into eight types of road projects. The manual CTD system consists of nine templates, one general template for Tier I type category and eight templates for Tier II type category. The CTDS user supplies the system with actual work quantities for established controlling activities for a project and by applying average or project specific production rates, durations for each controlling activity can be calculated. A standalone computer software was developed using VB.Net linked with Microsoft Access database and Microsoft Project for estimating contract time in working days. This software is recommended to be used in ODOT for effectively running the contract time determination system. The major benefit of this system to ODOT is that its continuous use would provide a structured approach towards contract time estimation. This system will expedite the contract time estimation process, provide documentation for a stronger defense in contract time disputes and allow less experienced schedulers to gain confidence as they learn how to estimate reasonable and realistic contract times.School of Civil & Environmental Engineerin

Reliable Communication in Wireless Networks

Wireless communication systems are increasingly being used in industries and infrastructures since they offer significant advantages such as cost effectiveness and scalability with respect to wired communication system. However, the broadcast feature and the unreliable links in the wireless communication system may cause more communication collisions and redundant transmissions. Consequently, guaranteeing reliable and efficient transmission in wireless communication systems has become a big challenging issue. In particular, analysis and evaluation of reliable transmission protocols in wireless sensor networks (WSNs) and radio frequency identification system (RFID) are strongly required. This thesis proposes to model, analyze and evaluate self-configuration algorithms in wireless communication systems. The objective is to propose innovative solutions for communication protocols in WSNs and RFID systems, aiming at optimizing the performance of the algorithms in terms of throughput, reliability and power consumption. The first activity focuses on communication protocols in WSNs, which have been investigated, evaluated and optimized, in order to ensure fast and reliable data transmission between sensor nodes. The second research topic addresses the interference problem in RFID systems. The target is to evaluate and develop precise models for accurately describing the interference among readers. Based on these models, new solutions for reducing collision in RFID systems have been investigated

A wavelet-based system for event detection in online real-time sensor data

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.Page 78 blank.Includes bibliographical references (p. 74-77).Sensors are increasingly being used for continuous monitoring purposes, the process of which generates huge volumes of data that need to be mined for interesting events in real-time. The purpose of this research is to develop a method to identify these events, and to provide users with an architecture that will allow them to analyze events online and in real-time, to act upon them, and to archive them for future offline analysis. This thesis is divided into two major portions. The first discusses a general software architecture that performs the functions defined above. The architecture proposed assumes no prior knowledge of the data, and is capable of dealing with multi-source data feed from any type of sensor(s) on one end, and can handle multiple clients on the other. The second part of the thesis discusses a wavelet-based algorithm for detecting certain types of events in real-time in one-dimensional numeric time-series data. Wavelets were judged to be the most appropriate technique for analyzing random sensor signals for which no prior information is available. The wavelet-based method in addition allows users to delve into different levels of abstraction (based on varying time periods) while looking at the data, which cannot be done by any previous method for real-time event detection. This thesis also touches on the fundamental question of how one defines an event, which is more easily possible in a particular domain, for a specific purpose, but is much harder to do in a generic, domain-independent level.by Charuleka Varadharajan.S.M