Developments in Estimation and Control for Cloud-Enabled Automotive Vehicles.

Cloud computing is revolutionizing access to distributed information and computing resources that can facilitate future data and computation intensive vehicular control functions and improve vehicle driving comfort and safety. This dissertation investigates several potential Vehicle-to-Cloud-to-Vehicle (V2C2V) applications that can enhance vehicle control and enable additional functionalities by integrating onboard and cloud resources. Firstly, this thesis demonstrates that onboard vehicle sensors can be used to sense road profiles and detect anomalies. This information can be shared with other vehicles and transportation authorities within a V2C2V framework. The response of hitting a pothole is characterized by a multi-phase dynamic model which is validated by comparing simulation results with a higher-fidelity commercial modeling package. A novel framework of simultaneous road profile estimation and anomaly detection is developed by combining a jump diffusion process (JDP)-based estimator and a multi-input observer. The performance of this scheme is evaluated in an experimental vehicle. In addition, a new clustering algorithm is developed to compress anomaly information by processing anomaly report streams. Secondly, a cloud-aided semi-active suspension control problem is studied demonstrating for the first time that road profile information and noise statistics from the cloud can be used to enhance suspension control. The problem of selecting an optimal damping mode from a finite set of damping modes is considered and the best mode is selected based on performance prediction on the cloud. Finally, a cloud-aided multi-metric route planner is investigated in which safety and comfort metrics augment traditional planning metrics such as time, distance, and fuel economy. The safety metric is developed by processing a comprehensive road and crash database while the comfort metric integrates road roughness and anomalies. These metrics and a planning algorithm can be implemented on the cloud to realize the multi-metric route planning. Real-world case studies are presented. The main contribution of this part of the dissertation is in demonstrating the feasibility and benefits of enhancing the existing route planning algorithms with safety and comfort metrics.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120710/1/zhaojli_1.pd

Applications of Internet of Things

This book introduces the Special Issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITSs), (II) location-based services (LBSs), and (III) sensing techniques and applications. Three papers on ITSs are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBSs are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by Gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al

Evolving Clustering Algorithms And Their Application For Condition Monitoring, Diagnostics, & Prognostics

Applications of Condition-Based Maintenance (CBM) technology requires effective yet generic data driven methods capable of carrying out diagnostics and prognostics tasks without detailed domain knowledge and human intervention. Improved system availability, operational safety, and enhanced logistics and supply chain performance could be achieved, with the widespread deployment of CBM, at a lower cost level. This dissertation focuses on the development of a Mutual Information based Recursive Gustafson-Kessel-Like (MIRGKL) clustering algorithm which operates recursively to identify underlying model structure and parameters from stream type data. Inspired by the Evolving Gustafson-Kessel-like Clustering (eGKL) algorithm, we applied the notion of mutual information to the well-known Mahalanobis distance as the governing similarity measure throughout. This is also a special case of the Kullback-Leibler (KL) Divergence where between-cluster shape information (governed by the determinant and trace of the covariance matrix) is omitted and is only applicable in the case of normally distributed data. In the cluster assignment and consolidation process, we proposed the use of the Chi-square statistic with the provision of having different probability thresholds. Due to the symmetry and boundedness property brought in by the mutual information formulation, we have shown with real-world data that the algorithm’s performance becomes less sensitive to the same range of probability thresholds which makes system tuning a simpler task in practice. As a result, improvement demonstrated by the proposed algorithm has implications in improving generic data driven methods for diagnostics, prognostics, generic function approximations and knowledge extractions for stream type of data. The work in this dissertation demonstrates MIRGKL’s effectiveness in clustering and knowledge representation and shows promising results in diagnostics and prognostics applications

Modeling Vessel Behaviours By Clustering Ais Data Using Optimized DBSCAN

Today, maritime transportation represents substantial international trade. Sustainable development of marine transportation requires systematic modeling and surveillance for maritime situational awareness. In this research thesis, we present an enhanced density-based spatial clustering (DBSCAN) method to model vessel behaviors. The proposed methodology enhances the DBSCAN clustering performance by integrating the Mahalanobis Distance metric that considers the correlations of the points representing the locations of the vessels. The clustering method is applied to historical Automatic Identification System (AIS) data and generates an action recommendation tool and a model for detecting vessel trajectory anomalies. Two case studies present outcomes from the openly available Gulf of Mexico AIS data, and Saint Lawrence Seaway and Great Lakes AIS licensed data acquired from ORBCOMM (a maritime AIS data provider). This research proposes a framework for modeling AIS data, an algorithm for generating a clustering model of the vessels' trajectories, and a model for detecting vessel trajectory anomalies such as unexpected stops, deviations from regulated routes, or inconsistent speed. This work's findings demonstrate the applicability and scalability of the proposed method for modeling more water regions, contributing to situational awareness, vessel collision prevention, safe navigation, route planning, and detection of vessel behavior anomalies for auto-vessels development

Real-Time Prediction and Decision Making in Connected and Automated Vehicles Under Cyber-Security and Safety Uncertainties

Our current transportation system is on the brink of transforming into a highly connected,automated, and intelligent system as a result of the rapid emergence of connected andautomated vehicles (CAVs). CAVs, with various levels of automation, are expected toincrease overall road safety, reduce travel time, improve comfort, improve fuel efficiency, anddecrease fatal accidents in the near future. CAVs use a combination of cameras, ultrasonicsensors, and radar to build a digital map of their surroundings and operate the vehicleaccordingly. As a result, there are numerous sources of information that can be manipulated,with malicious or non-malicious intent, which may result in dangerous situations. Althoughthe ever-increasing use of CAV technologies in vehicles are expected to have numerousadvantages, they can give rise to new challenges in terms of safety, security, and privacy.As evident by recent crash records and experiments successfully conducting cyber attacks onvehicles, the currently available autonomous systems lack the ability to fully handle novel,complex situations. Hence, the potential drawbacks of CAVs are not negligible and shouldnot be ignored. In this study, we investigate the real-time prediction and decision makingin CAVs under cyber-security and safety uncertainties

Machine learning for Internet of Things data analysis: A survey

Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this Big Data is the key to developing smart IoT applications. This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case. The key contribution of this study is presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for a more detailed exploration.Comment: Digital Communications and Networks (2017

Behavioral model based trust management design for IoT at scale

With the rise in the number of devices in the Internet of Things (IoT), the number of malicious devices will also drastically increase. Smart cities\u27 decisions are based on data being collected by IoT devices in real-time, of which a connected-vehicle system is included. Behaviors such as malicious data injection can significantly impact connected vehicles. To aid in combating this threat, monitoring smart city and connected vehicle\u27s sensor data will allow for construction of a behavioral model. Implementing machine learning will aid in constructing a standard behavior such that any device that begins to malfunction or behave maliciously can be detected and mitigated in real-time. This behavioral analysis will be further applied to supplement trust management approaches such that a more accurate value can be associated with the device\u27s perceived trustworthiness without the need to rely on a majority consensus