It is envisaged that 5G can enable many vehicular use cases that require high capacity, ultra-low latency and high reliability. To support this, 5G proposes the use of dense small cells technology as well as and highly directional mmWave systems deployment, among many other new advanced communication technologies, to boost the network capacity, reduce latency and provide high reliability. In such systems

Driverless shuttles are a new automated road-based means of transport, small in size and capacity and with a relatively low operational speed. Classified as high-automation vehicles, these shuttles are capable of driverless operations in specific operational design domains. Given their characteristics, driverless shuttles have been studied as a first/last-mile complement to main public transport lines

Intelligent vehicles and Advanced Driver Assistance Systems (ADAS) are being developed rapidly over the past few years. Many applications such as vehicle localization, environment perception, and path planning have shown promising potentialities. While there is great interest in migrating from complete human-controlled vehicles towards fully autonomous vehicles, it is natural that researchers spending

Vehicles are becoming increasingly autonomous and connected, leading to an increase in the types of security threats to vehicles. Controller Area Network (CAN) is a serial bus system that is used to connect sensors and controllers (Electronic Control Units – ECUs) within a vehicle. ECUs vary widely in processing power, storage, memory, and connectivity. There is a need for efficient security countermeasures

In recent years, the concept of non-orthogonal multiple access (NOMA) has gathered much attention due to its potential to offer high spectral efficiency, present user fairness and grant free access to sixth generation (6G) vehicular networks. This paper proposes a new optimization framework for NOMA-enabled cooperative vehicular network. In particular, we jointly optimize the vehicle paring, channel

The introduction of automated vehicles (AVs) leads to mixed traffic where the AV should safely and efficiently integrate into established interactions. Since the driving behavior and thus the AV’s communication may differ from that of traditional road users, highly comprehensible communication strategies are needed to reliably convey the AV’s intention. This work investigates the encounter of an AV

Use of distributed ledger technologies like blockchain is becoming more common in transportation/mobility ecosystems. However, cyber-security failures may occur at places where the blockchain system connects with the real world. In this paper, we propose a novel risk assessment framework for blockchain applications in smart mobility. We aim at systematically quantifying the risk by presenting ordinal

We present a Long Short Term Memory (LSTM) encoder-decoder architecture to anticipate the future positions of vehicles in a road network given several seconds of historical observations and associated map features. Unlike existing architectures, the proposed method incorporates and updates the surrounding vehicle information in both the encoder and decoder, making use of dynamically predicted new data

Professional drivers are required to safely transport passengers and/or properties of customers to their destinations, so they must keep being mentally and physically healthy. Health problems will largely affect driving performance and sometimes cause loss of consciousness, which results in injury, death, and heavy compensation. Conventional systems can detect the loss of consciousness or urgently

With the high data rates and ultra-low latency it provides, millimeter-wave (mmWave) communications will be a key enabler for future vehicular networks. However, due to high penetration losses and high mobility, mmWave links experience frequent blockages. We present an analytical framework to evaluate the performance of vehicular relaying, where vehicles on a highway exchange data with the network

This paper provides an introduction to the opportunities for improving the performance of road transportation automation systems by use of vehicle-vehicle and vehicle-infrastructure communication and cooperation. Four different types of cooperative driving automation are defined and examples of the functionality enabled by each are described. Although the benefits of cooperative automation are significant

This paper discusses the feasibility of data captured in a long-term Naturalistic Driving Study (NDS) for identification of vehicle dynamics. Driving data were captured for over a year. In this data capture, there was minimal effort to define or control everyday driving practices. While the use of real-world data for model parameter identification is a well-known method, NDS are commonly used to explore

The students generally lack the overall grasp about navigation and the operation practice of equipment and instruments on board under the current education mode. The simulator based on virtual reality has become an important way in learning and training. As a result, this paper develops a whole ship simulation training platform for the teaching and training of navigation. Occlusion Culling and Levels

Smart card data from the Automatic Fare Collecting systems (AFC) and timetable information, such as Automatic Vehicle Location (AVL), are used in combination by practitioners and researchers to gain a deeper understanding of the public transit network. In some cases, AVL data are not available due to records being missing in the system. In such cases, people resort to the used schedule timetable such

Vehicle Classification (VC) is a key element of Intelligent Transportation Systems (ITS). Diverse ranges of ITS applications like security systems, surveillance frameworks, fleet monitoring, traffic safety, and automated parking are using VC. Basically, in the current VC methods, vehicles are classified locally as a vehicle passes through a monitoring area, by fixed sensors or using a compound method

Route scheduling is crucial for uninterrupted operation of modern bus fleets consisting of electric buses. This paper proposes an exact route scheduling optimization model for centralized bus depots based on mixed integer linear programming. In order to adjust to the current situation at many electric bus depots, the model considers a heterogeneous fleet consisting of multiple types of electric buses

In Cooperative Intelligent Transportation Systems (C-ITS), vehicles can communicate with one another and with the infrastructure. The openness of the transportation system to new functionalities and use cases may create new vulnerabilities that must be taken into account. In this paper, we study three main use cases, the effectiveness and performance evaluation of certificate reloading in real environment

The industry is providing vehicles with advanced features and technologies that allows vehicles to connect and communicate with their nearby environment. These technologies’ umbrella term, Cooperative Intelligent Transportation Systems (C-ITS) are aimed to enhance road traffic efficiency, safety and assist the drivers in multiple ways. The C-ITS communication system should be able to offer the functional

Car-following theory has received considerable attention as a core component of Intelligent Transportation Systems. However, its application to the emerging autonomous vehicles (AVs) remains an unexplored research area. AVs are designed to provide convenient and safe driving by avoiding accidents caused by human errors. They require advanced levels of recognition of other drivers’ driving-style. With

Safety guarantees and regulatory approval for autonomous vehicles remain an ongoing challenge. In particular, software that is frequently adapted or contains complex, non-transparent components, such as artificial intelligence, is exceeding the limits of safety standards. This paper presents a detailed implementation of an online verification module – the Supervisor – that copes with these challenges

The fifth generation (5G) of wireless networks promises to meet the stringent requirements of vehicular use cases that cannot be supported by previous technologies. However, the stakeholders of the automotive industry (e.g., car manufacturers and road operators) are still skeptical about the capability of the telecom industry to take the lead in a market that has been dominated by dedicated intelligent

Freight transport disruptions in recent years caused production shutdowns in the industry, supply shortages and high economic damage around the world. In practice, contingency strategies from transport operators are often weak and disruption management fails to handle the situation sufficiently. Despite availability of data collection, academic optimization approaches, and multiple initiatives for

Intelligent intersections have the potential to serve as an integral part of tomorrow’s traffic infrastructure. Wireless communication is key to enabling such technology. We consider a scenario where two flows of vehicles traverse an intelligent intersection. We investigate safety in emergency braking scenarios, where one of the vehicles in a flow suddenly decides to emergency brake and emergency braking

Unmanned Aircraft Systems (UAS) provide the platform of many applications such as efficient package delivery, monitoring, surveillance, search and rescue, and more. As the density of UAS deployed in urban areas for delivery and monitoring operations is expected to increase in the near future, it becomes critical to design an efficient collision-free UAS path planning system to be used by a centralized

In this article, we present an incentive mechanism for Vehicular Crowdsensing in the context of autonomous vehicles (AVs). In particular, we propose a solution to the problem of sensing coverage of regions located out of the AVs’ planned trajectories. We tackle this problem by dynamically modifying the AVs’ trajectories and collecting sensing samples from regions otherwise unreachable by originally

What a year to start a new journal! 2020 was the inaugural year of the IEEE Open Journal of Intelligent Transportation Systems. We are delighted that already so many authors have submitted their work to “OJ-ITS,” it shows that there is a lot of interest in the ITS community for a Gold Open Access Journal.

We present a semi-automatic annotation method to build a large dataset for traffic landmark detection, where traffic landmarks include traffic signs, traffic lights as well as road markings. Labor-intensive bounding box tagging is a huge challenge to generate a large dataset for detection algorithms. To mitigate the labor, we adopt a high-definition (HD) map and a positioning system. We propose a process

High-capacity public transport services such as metro and commuter trains are efficient during normal operations but are vulnerable to disruptions. To manage disruptions, bridging buses are commonly called in to replace the rail-based service along the disrupted lines. These often take significant time to arrive and are costly to keep stand-by. Demand-responsive transport such as taxi can respond to

The rapid expansion of intra-vehicle networks has increased the number of threats to such networks. Most modern vehicles implement various physical and data-link layer technologies. Vehicles are becoming increasingly autonomous and connected. Controller Area Network (CAN) is one such serial bus system that is used to connect sensors and controllers (Electronic Control Units—ECUs) within a vehicle.

Despite rapid advances in automated text processing, many related tasks in transit and other transportation agencies are still performed manually. For example, incident management reports are often manually processed and subsequently stored in a standardized format for later use. The information contained in such reports can be valuable for many reasons: identification of issues with response actions

There is a pressing need for road authorities to take a proactive role in the deployment of automated vehicles on the existing road network. This requires a comprehensive understanding of the driving environment characteristics that affect the performance of automated vehicles. In this context, a field test with Lane Departure Warning (LDW) and Lane Keeping Systems (LKS)-enabled vehicles was conducted

Machine learning algorithms aim to improve the power of predictors over conventional regression models. This study aims to tap the predictive potential of crash mechanism-related variables using ensemble machine learning models. The results demonstrate selected models can predict severity at a high level of accuracy. The stacking model with a linear blender is preferred for the designed ensemble combination

Traffic light control falls into two main categories: Agnostic systems that do not exploit knowledge of the current traffic state, e.g., the positions and velocities of vehicles approaching intersections, and holistic systems that exploit knowledge of the current traffic state. Emerging fifth generation (5G) wireless networks enable Vehicle-to-Infrastructure (V2I) communication to reliably and quickly

Existing microscopic traffic models represent the lane-changing maneuver as a continuous and uninterrupted lateral movement of the vehicle from its original to the target lane. We term this representation as Continuous Lane-Changing (CLC). Recent empirical studies find that not all lane-changing maneuvers are continuous; the lane-changer may pause its lateral movement during the maneuver resulting

Trustworthy environment perception is the fundamental basis for the safe deployment of automated agents such as self-driving vehicles or intelligent robots. The problem remains that such trust is notoriously difficult to guarantee in the presence of systematic faults, e.g., non-traceable errors caused by machine learning functions. One way to tackle this issue without making rather specific assumptions

Current urbanization trends are leading to heightened demand of smarter technologies to facilitate a variety of applications in intelligent transportation systems. Automated crowdsensing constitutes a strong base for ITS applications by providing novel and rich data streams regarding congestion tracking and real-time navigation. Along with these well-leveraged data streams, drivers and passengers tend

The introduction of automated vehicles means that some or all operational control over these vehicles is diverted away from a human driver to a technological system. The concept of Meaningful Human Control (MHC) was derived to address control issues over automated systems, allowing a system to explicitly consider human intentions and reasons. Applying MHC to technological systems, such as automated

Recently, individual electric mobility gains significance due to legislation and social discussion. Customers demand longer battery ranges. Advanced planning is a different and more sustainable approach. Potentially, they assist drivers in exploiting the installed range on long journeys. Earlier research of the authors showed that an optimal combination of speed, charging choice and amount potentially

Traffic congestion in urban areas causes economic and time loss. Such traffic congestion is caused by selfish routing where users aim to minimize their own travel time. Even if a navigation system provides them with recommended routes, they may not follow the routes, due to dissatisfaction with the expected travel time. In this article, inspired by the concept of “Nudge,” we propose “selfish yet optimal

Deviations between system current measurements and real values in the power train of Electric Vehicles (EVs) can cause severe problems. Among others, these are restricted performance and cruising range. In this work, we propose a fleet-based framework to correct such deviations. We assume that the real value is the mean of all identically constructed EVs’ measurements for the same input. Under this

Traffic congestion is a major societal challenge. By advising drivers on the optimal lane to drive, traffic flow can be improved, and congestion reduced. In this paper we describe the development of a lane-specific advice Human Machine Interface (HMI). Persuading drivers to follow an advice that is beneficial to the traffic situation, but may not be immediately beneficial to the drivers themselves

This paper presents a novel blockchain-based energy trading architecture for electric vehicles (EVs) within smart cities. By allowing local renewable energy providers to supply public charging stations, EV drivers can gain access to affordable energy and optimally plan for their charging operations. For this purpose, we present a smart-contract based trading platform that runs on top of a private Ethereum

Increasing demands on reliability and safety of automated driving functions require an augmented usage of simulation tools for the efficient calibration of these functions. However, finding an optimal solution can be costly, especially when the objective function is represented by scenario simulations. To face these challenges, a novel optimization scheme for simulation based calibration problems,

Cooperation is an enabler for autonomous vehicles. A promising application of cooperative driving is high-density platooning, where trucks drive with low inter-vehicle distances. It aims at increasing the road and fuel efficiency whilst guaranteeing safety. The safe and efficient coordination of the control requires the regular and reliable exchange of V2V messages. The performance of the vehicular

Internet of Things (IoT) and Cloud Computing (CC) technologies are becoming critical requirements to the advancement of intelligent transportation systems (ITSs). ITSs generally rely on captured images to evaluate the status of traffic and perform vehicle statistics. However, such images may contain confidential information, and thus, securing such images is paramount. Therefore, we propose in this

License plate recognition (LPR) technology has been used to combat vehicle-related crime in urban areas in many developed contexts. However, commercially available LPR systems are expensive and not feasible for large scale adoption in developing countries. The development of a low-cost crowd-sourced solution requires an informed approach to the selection of an appropriate camera, as well as a realistic

Connected and autonomous vehicles have been significantly studied. They are connected to a network and communicate by exchanging information with each other, so they can detect blind spots that cannot be recognized by non-connected (conventional) vehicles. Therefore, they are expected to contribute to traffic efficiency and safety. However, even if connected vehicles are put to practical use in the

Dear readers, It is with pleasure and pride that we present the first issue of the IEEE Open Journal of Intelligent Transportation Systems (OJ-ITS). Responding to a world-wide demand for Gold Open Access Journals, ‘OJ-ITS’ has been developed as one of seven new Open Journals published by IEEE. Published by the IEEE Intelligent Transportation System Society, OJ-ITS publishes both scientific as well

This paper presents a surround vehicle motion prediction algorithm for multi-lane turn intersections using a Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN). The motion predictor is trained using the states of subject and surrounding vehicles, which are collected by sensors mounted on an autonomous vehicle. Data on 484 vehicle trajectories were collected from real traffic situations

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