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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