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Intelligent Transportation Systems Magazine, IEEE

Issue 3 • Date Fall 2013

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Displaying Results 1 - 19 of 19
  • [Front cover]

    Page(s): C1
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  • [Table of contents]

    Page(s): 1
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  • Editorial Board

    Page(s): 2
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  • Expanding and Improving [Editor's Column]

    Page(s): 2 - 5
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  • Special Issue on "Human Factors in Intelligent Vehicles"

    Page(s): 3
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  • Bridging Academia an Industry in ITS [President's Message]

    Page(s): 4 - 5
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  • Special Issue on IEEE IV 2012 Workshops: Part 1 of 2 [Guest Editorial]

    Page(s): 6 - 7
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  • Vehicle Classification with Confidence by Classified Vector Quantization

    Page(s): 8 - 20
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4875 KB) |  | HTML iconHTML  

    Automated vehicle classification based on static images is highly practical and directly applicable for various operations such as traffic related investigations. An integrated vehicle detection and classification system is proposed in this paper. A multi-resolution vehicle detection scheme is introduced as an improvement over the cascade boosted classifiers proposed recently by Negri et al. 2008 in the literature. Building on solutions from previous works from Negri et al, the implementation of a new decision strategy renders current detection method to be robust to environmental changes. The vehicle classification is based on the Classified Vector Quantization (CVQ) proposed earlier by Zhang et al. 2009. The justification of choosing CVQ is its advantages in providing classification confidence by incorporating rejection option. The significance of rejection in enhancing the system?s reliability is emphasized and evaluated. A database composed of more than 2800 images of four types of vehicles (cars, vans, light trucks and buses) was created using police surveillance cameras. The proposed scheme offers a performance accuracy of over 95% with a rejection rate of 8%, and reliability over 98% with a rejection rate of 20%. This exhibits promising potentials for implementations into real-world applications. View full abstract»

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  • Reliable Positioning Domain Computation for Urban Navigation

    Page(s): 21 - 29
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1917 KB) |  | HTML iconHTML  

    Reliable positioning is a key issue for intelligent vehicle navigation. Interval-based positioning methods have shown to be capable of computing relevant confidence domains used for integrity monitoring in environments which are challenging for Global Positioning System (GPS). The approach presented in this paper consists in tightly coupling a GPS receiver with a 3D-map of the drivable area. Interval analysis is employed to solve the constraint positioning problem using contractions and bisections. Integrity is provided through the use of a robust set inversion scheme applied to a redundant measurement set. If the prior distribution of the measurement noise is known, it is possible to compute confidence domains that correspond to a given integrity risk, which is often set very low out of safety considerations. In this paper we examine a way of validating the proposed approach, using a real experimental dataset and a ground truth equipment. Different tunings of the method, corresponding to different risks, are assessed in terms of availability and integrity in order to compute statistical metrics. Results indicate that this methodology is relevant since the specified risk corresponds to experimental observations. View full abstract»

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  • Road Side Unit Deployment: A Density-Based Approach

    Page(s): 30 - 39
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3262 KB) |  | HTML iconHTML  

    Currently, the number of vehicles increases every year, raising the probability of having accidents. When an accident occurs, wireless technologies enable vehicles to share warning messages with other vehicles by using vehicle to vehicle (V2V) communications, and with the emergency services by using vehicle to infrastructure (V2I) communications. Regarding vehicle to infrastructure communications, Road Side Units (RSUs) act similarly to wireless LAN access points, and can provide communications with the infrastructure. Since RSUs are usually very expensive to install, authorities limit their number, especially in suburbs and areas of sparse population, making RSUs a precious resource in vehicular environments. In this paper, we propose a Density-based Road Side Unit deployment policy (D-RSU), specially designed to obtain an efficient system with the lowest possible cost to alert emergency services in case of an accident. Our approach is based on deploying RSUs using an inverse proportion to the expected density of vehicles. The obtained results show how D-RSU is able to reduce the required number of RSUs, as well as the accident notification time. View full abstract»

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  • Circumventing the Feature Association Problem in SLAM

    Page(s): 40 - 58
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5396 KB) |  | HTML iconHTML  

    In autonomous applications, a vehicle requires reliable estimates of its location and information about the world around it. To capture prior knowledge of the uncertainties in a vehicle's motion response to input commands and sensor measurements, this fundamental task has been cast as probabilistic Simultaneous Localization and Map building (SLAM). SLAM has been investigated as a stochastic filtering problem in which sensor data is compressed into features, which are consequently stacked in a vector, referred to as the map. Inspired by developments in the tracking literature, recent research in SLAM has recast the map as a Random Finite Set (RFS) instead of a random vector, with huge mathematical consequences. With the application of recently formulated Finite Set Statistics (FISST), such a representation circumvents the need for fragile feature management and association routines, which are often the weakest component in vector based SLAM algorithms. This tutorial demonstrates that true sensing uncertainty lies not only in the spatial estimates of a feature, but also in its existence. This gives rise to sensor probabilities of detection and false alarm, as well as spatial uncertainty values. By re-addressing the fundamentals of SLAM under an RFS framework, it will be shown that it is possible to estimate the map in terms of true feature number, as well as location. The concepts are demonstrated with short range radar, which detects multiple features, but yields many false measurements. Comparison of vector, and RFS SLAM algorithms shows the superior robustness of RFS based SLAM to such realistic sensing defects. View full abstract»

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  • A New Modeling Based on Urban Trenches to Improve GNSS Positioning Quality of Service in Cities

    Page(s): 59 - 70
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2554 KB) |  | HTML iconHTML  

    Digital maps with 3D data proved to make it possible the determination of Non-Line-Of-Sight (NLOS) satellites in real time, whilst moving, and obtain significant benefit in terms of navigation accuracy. However, such data are difficult to handle with Geographical Information System (GIS) embedded software in real time. The idea developed in this article consists is proposing a method, light in terms of information contents and computation throughput, for taking into account the knowledge of the 3D environment of a vehicle in a city, where multipath phenomena can cause severe errors in positioning solution. This method makes use of a digital map where homogeneous sections of streets have been identified, and classified among different types of urban trenches. This classification is so called: "Urban Trench Model". Not only NLOS satellites can be detected, but also, if needed, the corresponding measurements can be corrected and further used in the positioning solver. The paper presents in details the method and its results on several real test sites, with a demonstration of the gain obtained on the final position accuracy. The benefit of the Urban Trench Model, i.e. the reduction of positioning errors as compared to conventional solver considering all satellites, gets up to an amount between 30% and as much as 70% e.g. in Paris. View full abstract»

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  • IEEE Transactions on Intelligent Transportation Systems

    Page(s): 71
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  • Dr. Mashrur (Ronnie) Chowdhury [ITS People]

    Page(s): 72 - 73
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  • Review of "Autonomous Intelligent Vehicles: Theory, Algorithms, and Implementation" (Cheng, H.; 2011) [Book review]

    Page(s): 74 - 76
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  • Special Issue on Perception and Planning for Autonomous Vehicles

    Page(s): 75
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  • Multitude

    Page(s): 77
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  • [Calendar]

    Page(s): 78
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  • ITSolves ITSelf [ITS Fun]

    Page(s): C3
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Aims & Scope

The IEEE Intelligent Transportation Systems Magazine (ITSM) publishes peer-reviewed articles that provide innovative research ideas and application results, report significant application case studies, and raise awareness of pressing research and application challenges in all areas of intelligent transportation systems. 

Full Aims & Scope

Meet Our Editors

Editor-in-Chief


Miguel Ángel Sotelo

Department of Computer Engineering

University of Alcalá