This article focuses on the feasibility of commercial cloud services for connected vehicle (CV) applications in a transportation cyberphysical systems (TCPS) environment. TCPS implies that CVs, in addition to being connected with each other, communicate with the transportation and computing infrastructure to fulfill application requirements. The motivation of this study is to accelerate commercial

As automotive data increase in scale and value, it is important to understand who owns and has the ability to access information generated by connected vehicles. The plummeting costs of sensing, connectivity, and storage mean that original equipment manufacturers (OEMs), their suppliers, and end users have access to more realtime data than ever [1]. Data collection is complicated by the public?s perception

Traffic control systems (TCSs) are an emerging focus area among transportation technologies. More than 55,435 TCS technologies were patented during the last decade, and researchers, policy makers, and decision makers are increasingly interested in turning TCSs into safe and environmentally friendly transportation realities. This article analyzes all TCS-related patents issued during the period of 2009

In prior research, a statistically cheap method was developed to monitor transportation network performance by using only a few groups of agents without having to forecast the population flows. The current study validates this multiagent inverse optimization (MAIO) method using taxi GPS trajectory data from the city of Wuhan, China. Using a controlled 2,062-link network environment and different GPS

The Research Group on Sustainable Mobility and Railway Engineering (SUM+LAB) at the University of Cantabria (UC), Spain, is dedicated to the development, research, and transfer of knowledge in the field of sustainable mobility and railway engineering. It also promotes intelligent, connected, shared, and low-emission mobility, as well as the efficient use and optimization of mobility systems.

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It is crucial to test and evaluate the intelligence capacity of driverless vehicles, which impacts the commercialization process of autonomous cars and trucks. This overview aims to summarize the challenging problems in testing and evaluating the intelligence capacity of driverless vehicles. First, the grading of the behavioral intelligence of driverless vehicles is described. Then, to achieve a quantitative

One of the indispensable functions of a self-driving vehicle is to estimate its dynamic world, which includes various traffic participants within complex driving scenarios. The estimation mechanism has to be flexible, fast, and robust; however, achieving these requirements is still challenging. Dynamic grid maps are one of the possible ways to combine and estimate multisensory information at an intermediate

Taxis are an important transportation mode in many cities due to their convenience and accessibility. In the taxi-dispatching problem, sometimes it is more beneficial for the supplier if taxis cruise in the network after serving the first request to pick up the next passenger, while sometimes it is better that they wait in stations for new trip requests. In this article, we propose a rolling-horizon

The representation, visualization, and modeling of traffic data is at the heart of intelligent transportation systems. Different types of traffic data exist, and novel ways of their accurate representation and modeling, which are useful for further analyses, simulations, and optimizations, are sought. In this work, location-specific traffic flows are represented by finite mixtures of circular normal

Modeling and analyzing metro station ridership is of great importance to passenger flow management and transportation planning operations. In practice, ridership can be affected by multiple factors, including spatial factors (distance and network topology), temporal factors (e.g., period and trend), and external factors (e.g., land use and socioeconomics). However, existing studies mainly focus on

Mobility-on-demand (MOD) systems consisting of shared autonomous vehicles (SAVs) are expected to improve the efficiency of urban transportation through reduced vehicle ownership and parking demand. However, several issues related to their implementation remain open, such as unifying the vehicle and ridesharing (RS) assignment with rebalancing (RB) unoccupied vehicles.

Mobility on demand has been gaining more attention from the research community as a way to offer smart and efficient transportation services to people. Despite the advancements in vehicular technologies, vehicle breakdown (VB) remains one of the major contributors to the disruption of fleet operations, which may inflict large recovery costs and damage the service provider?s reputation. However, modeling

The Queensland University of Technology (QUT) Transport Research Group (Figure 1) focuses on advancing knowledge, engineering, and science in traffic management and control; travel behavior; performance evaluation; traffic safety; data analytics; and public and active transportation. Our work is underpinned by a strong intelligent transportation systems foundation. We have a long-standing, productive

Recently, the traditional taxi industry has been struggling to keep its market share, especially with the emergence of new transport network companies (e.g., Uber). One of the problems with traditional taxi services is the difficulty of matching the taxi demand to its supply when there is no phone-booking or other reservation system. From that perspective, the taxi driver?s experience is important

This article provides a review of the production and uses of maps for autonomous driving and a synthesis of the opportunities and challenges. For many years, maps have helped human drivers make better decisions, and in the future, maps will continue to play a critical role in enabling safe and successful autonomous driving. There are, however, many technical, societal, economic, and political challenges

Digital quadruplets aiming to improve road safety, traffic efficiency, and driving cooperation for future connected automated vehicles are proposed with the enlightenment of ACP-based parallel driving. The ACP method denotes artificial societies, computational experiments, and parallel execution modules for cyberphysical-social systems. Four agents are designed in the framework of digital quadruplets:

Eco-driving through multiple intersections has significant fuel benefit for road transportation. Most existing studies assume that vehicles travel at a constant speed between intersections, which naturally leads to large errors in fuel prediction and trajectory planning. This article focuses on eco-driving operation through multiple signalized intersections considering more realistic powertrain dynamics

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This article provides an overview of the use of inertial and visual sensors and discusses their prospects in the Arctic navigation of autonomous vehicles. We also examine the fusion algorithms used thus far for integrating vehicle localization measurements as well as the map-matching (MM) algorithms relating position coordinates with road infrastructure. Our review reveals that conventional fusion

An effective way to detect the presence of a spoofing attack is to verify Global Navigation Satellite System (GNSS) data with measurements from other sensors, such as inertial navigation systems (INSs). In this article, uncoupled GNSS/INS-verification approaches are experimentally evaluated in an automotive context. The approaches are uncoupled in the sense that GNSS and INS sensors are operated independently

A framework for ground vehicle localization that uses cellular signals of opportunity (SOPs), a digital map, an inertial measurement unit (IMU), and a Global Navigation Satellite System (GNSS) receiver is developed. This framework aims to enable localization in an urban environment where GNSS signals could be unusable or unreliable. The proposed framework employs an extended Kalman filter (EKF) to

Positioning is a key function for autonomous vehicles that requires globally referenced localization information. Lidarbased mapping, which refers to simultaneous localization and mapping (SLAM), provides continuous positioning in diverse scenarios. However, SLAM error can accumulate through time. Besides, only relative positioning is provided by SLAM. The Global Navigation Satellite System (GNSS)

The lane-level localization of vehicles with low-cost sensors is a challenging task. In situations in which Global Navigation Satellite Systems (GNSSs) suffer from weak observation geometry or from the influence of reflected signals, the fusion of heterogeneous information presents a suitable approach for improving the localization accuracy. We propose a solution based on a monocular front-facing camera

Global Navigation Satellite System (GNSS) integrity is defined as a measure of trust that can be placed in the correctness of the information supplied by the total system. Initially developed for safetycritical applications in the aeronautic domain, this concept has attracted more and more attention from terrestrial GNSS-based applications in recent years. The main problem of integrity monitoring for

Future road applications will require positioning accuracy on the order of one or a few decimeters and a convergence time measured in seconds. A high-accuracy service (HAS) provided by the Global Navigation Satellite System becomes a key enabler to meet these requirements. In particular, the Galileo program has committed to provide a free HAS. This entails several design challenges; namely, to achieve

This article presents the most thorough study to date of vehicular carrier-phase differential Global Navigation Satellite System (CDGNSS) positioning performance in a deep urban setting unaided by complementary sensors. Using data captured during approximately 2 h of driving in and around the dense urban center of Austin, Texas, a CDGNSS system is demonstrated to achieve 17-cm-accurate 3D urban positioning

Inland navigation and shipping are important pillars of the European Transport System. To support the skipper during safety-critical operations, precise position, navigation, and timing (PNT) data are required. This work discusses the role of PNT information for enabling inland waterway navigation-assistance functions, such as bridge-collision warning, mooring aiding, and automatic guidance. The real-time

Due to the ever-growing threat of GPS spoofing, it has become necessary for the aviation sector to develop an effective means of detection. This article focuses on two complementary spoofing-detection techniques that are available on commercial GPS receivers and thus require no additional hardware to operate. The primary methodology for detection is using a combination of radio power monitoring metrics

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This article presents a model-based method for fusing data from multiple sensors with a hypothesis-test-based component for rejecting potentially faulty or otherwise malign data. Our framework is based on an extension of the classic particle filter algorithm for real-time state estimation of uncertain systems with nonlinear dynamics with partial and noisy observations. This extension, based on classical

The Mechatronics Research and Development Group was created in 2002 by Prof. Gerardo Fern?ndez-L?pez and Prof. Juan Carlos Grieco of Simon Bol ivar University (USB) (Figure 1), Caracas, Venezuela. The group is focused on carrying out research and technological developments of excellence, both nationally and internationally. Its activities encompass mechatronics systems in general with special emphasis

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Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation, and deep learning applications. Despite this success, the validation of algorithm robustness has recently been recognized as a major challenge for the massive deployment of these new

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The objective of this dissertation is to develop a resilient control approach to secure cyberphysical systems (CPSs) against cyberattacks, communication network failures, and potential physical faults. A general CPS consists of three main components: 1) the physical part including sensors, actuators, and hardware of the plant; 2) the cyber part including the software and control ler logic of the plant;

The Complex Adaptive Systems for Transportation Laboratory (CASTLab) was established by Prof. Fei-Yue Wang in July 1999 for the task of designing and implementing the proposed intelligent traffic system for the city of Xinxiang, Henan, one of the first initiatives in intelligent transportation systems (ITS) in China. At the end of 1999, the CASTLab became a part of the newly created Center for Intelligent

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Connected and automated vehicles (CAVs) have the potential to address a number of safety, mobility, and sustainability issues of our current transportation systems. Cooperative longitudinal motion control is one of the key CAV technologies that allows vehicles to be driven in a cooperative manner to achieve system-wide benefits. In this paper, we provide a literature survey on the progress accomplished

How to collect the real-time information of unconnected vehicles has been a challenge for connected vehicle technologies. The LiDAR sensors deployed along the roadside and at intersections provide a solution to fill the data gap during the transition from the traditional traffic to the full connected traffic. The roadside LiDAR sensors can record the movement of all road users with a relative long

Within the next few years, autonomous vehicles will start being commercialized. In the same time, inter-vehicular communication is emerging. Using these new technologies allows designing new software to make cooperative cars. These cooperatives cars can exchange messages to improve traffic efficiency by, for instance, notifying about the presences of other cars, or managing the right-of-way at intersections

Autonomous lane keeping is an important safety feature for intelligent vehicles. This paper presents a lane keeping simulator that is built with image projections of recorded data in conjunction with vehicle dynamics estimation. An end-to-end learning method using convolutional neural network (CNN) takes front-view camera data as input and produces the proper steering wheel angle to keep the vehicle

Cooperative Adaptive Cruise Control (CACC) systems can increase roadway capacity, but the benefits are marginal at low market penetration rates (MPRs). Thus, a CACC dedicated lane is considered to group CACC vehicles for efficient traffic stream. Concepts of converting existing High Occupancy Vehicle (HOV) lanes into CACC lanes emerge, which leverages the infrastructural facilities and experience with

The breakpoints of fundamental diagrams (FDs) are an important foundation for estimating capacity, identifying traffic conditions, and determining the phase regime of multi-regime FDs. Unfortunately, to our best knowledge, there has been little research on determining breakpoints in a scientific way. This paper applies a Gaussian mixture model (GMM) to describe multi-component traffic conditions, and