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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

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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

Presents information on the The 2019 IEEE Intelligent Vehicles Symposium.

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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

Reports on Mission Laboratory B3: Infrastructure and Traffic Management in land transport (Lab B3). Lab3 focuses on modeling and optimization of transportation systems networks and traffic flow. It develops interfaces for the integration of intelligent transport Systems (ITS) and related processes in the field of traffic management of surface transport systems (including integration of related managing

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This paper proposes for the first time the use of an Internet of Things solution for the accurate recovery of incapacitated commercial and retail delivery drones. Since the use of drones will increase in popularity for a variety of uses, the problem of locating and recovering delivery drones is necessary. Using the emerging LoRaWAN and Sigfox networks (as examples of Low Power Wide Area Network (LPWAN)

Automated guided vehicles (AGVs) have been widely adopted in the logistic delivering of modern manufacturing. As a key performance index for an AGV, the positioning accuracy of commercial AGVs based on the traditional magnetic tracking approach is bigger than ?5mm, which cannot meet the requirement of many industrial applications. Thus, we proposed a novel magnetic tracking approach to improve the

The platooning of connected and automated vehicles has the potential to significantly improve the fuel efficiency of road transportation. Shortening the carfollowing distance to reduce aerodynamic drag is often used to improve fuel economy in today's platoons, but suffers high risk of rear-end collision. This paper presents an alternative solution to reduce platoon fuel consumption, i.e., periodic

Battery Electric Vehicles have a high potential in modern transportation, however, they are facing limited cruising range. The driving style, the road geometries including slopes, curves, the static and dynamic traffic conditions such as speed limits and preceding vehicles have their share of energy consumption in the host electric vehicle. Optimal energy management based on a semi-autonomous ecological

This paper focuses on a specific stochastic shortest path (SSP) problem, namely the punctuality problem. It aims to determine a path that maximizes the probability of arriving at the destination before a specified deadline. The popular solution to this problem always formulates it as a cardinality minimization problem by considering its data-driven nature, which is approximately solved by the 1 , -norm

The field of autonomous vehicles is an area of great importance nowadays among Intelligent Transport Systems. These autonomous vehicles must meet high standards of robustness, safety and protection to make autonomous driving safer than human driving, thus improving current accident rates and energy efficiency, so its complete implementation in the market is expected in the long term. However, there