Search

In recent years, Vehicle Make and Model Recognition (VMMR) has attracted a lot of attention as it plays a crucial role in Intelligent Transportation Systems (ITS). Accurate and efficient VMMR systems are required in real-world applications including intelligent surveillance and autonomous driving. The paper introduces a new large-scale dataset and a novel deep learning paradigm for VMMR. A new large-scale dataset dubbed Diverse large-scale VMM (DVMM) is proposed collecting image-samples with the most popular vehicle brands operating in Europe. A novel VMMR framework is proposed which follows a two-branch architecture performing make and model recognition respectively. A two-stage training procedure and a novel decision module are proposed to process the make and model predictions and compute the final model prediction. In addition, a novel metric based on the true positive rate is proposed to compare classification confusion of the proposed 2B–2S and the baseline methods. A complex experimental validation is carried out, demonstrating the generality, diversity, and practicality of the proposed DVMM dataset. The experimental results show that the proposed framework provides 93.95 % accuracy over the more diverse DVMM dataset and 95.85 % accuracy over traditional VMMR datasets. The proposed two-branch approach outperforms the conventional one-branch approach for VMMR over small-, medium-, and large-scale datasets by providing lower vehicle model confusion and reduced inter-make ambiguity. The paper demonstrates the advantages of the proposed two-branch VMMR paradigm in terms of robustness and lower confusion relative to single-branch designs. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) represent a new, growing segment of transportation research. While there have been prior studies and deployments of AVs worldwide, full autonomy in bus transit has gained interest among researchers and practitioners within the last decade, which presents an opportunity to synthesize early trends. Therefore, the objective of this paper is to provide a review of the latest research on fully autonomous buses to summarize findings and identify gaps needing future research. Forty studies were reviewed in detail, and five main themes were identified, which are (1) technology deployment; (2) user acceptance; (3) safety; (4) social and economic aspects; and (5) regulations, policies, and legal issues. The results reveal that most prior studies have focused on technology development, and the area of regulation and policy would benefit from additional study. Noteworthy differences between research in Europe and the United States were also identified. In Europe, large funded projects involving real-world deployments have focused on user acceptance, security and safety, costs, and related legal issues, whereas in the United States, research has primarily concentrated on simulation modelling with limited real-world deployments. The results of this review are important for policy-makers and researchers as AV technology continues to evolve and become more widely available. [ABSTRACT FROM AUTHOR]

Advancement in the fields of electrification, automation, and digitalisation and emerging social trends are fuelling the transformation of road transport resulting in the introduction of various innovative mobility solutions. Yet the reaction of people to many of the new solutions is still vastly unknown. This creates an unprecedented quandary for transport planners who are requested to design future transport systems and create the related investment plans without fully validated models to base the assessment upon. As some evidence on citizens' behaviour concerning new mobility solutions starts to be progressively made available, first attempts to update the existing models begin to emerge. Nevertheless, a lot more is needed as some of the transpiring mobility solutions have not yet reached the market, making the corresponding behaviour changes imponderable. In this context, the main purpose of this paper is to provide a review on how travel behaviour changes linked to the deployment of new mobility solutions have been considered in travel demand models. The new mobility solutions studied include carsharing, dynamic ridesharing, micromobility sharing services, and personal and shared autonomous vehicles. An overview and comparison of relevant studies implementing activity or trip-based demand models and other methodologies are presented. The analysis shows that the results of the different studies heavily depend on the extent to which behavioural changes are considered. The results of the review thus point to the need for holistic demand models that carefully mimic the urban reality with everything it has to offer and account for the importance of individual traits in the decision-making processes. Such models need an in-depth understanding of the microscopic mechanisms leading to the travel behaviour shifts linked to the most innovative mobility solutions. To achieve this level of detail, mobility living labs and their real-life experiments and experience with citizens, which are flourishing in Europe, are suggested to play a crucial role in the years to come. [ABSTRACT FROM AUTHOR]

• AV disengagement on public roads dominated by causes due to a planning issue. • AV disengagement induced by lacking certain numbers of radar and LiDAR sensors. • Drivers' take-over time impacted by disengagement cause and roadway characteristics. • To improve AV disengagements five or more radar sensors are needed. • Optimal number of LiDAR sensors to be installed is 3 or 4. Automated vehicles (AV) testing on the public roads is ongoing in several states in the US as well as in Europe and Asia. As long as the automated vehicle technology has not achieved full automation (Level 5), human drivers are still expected to take over the steering wheel and throttles when there is an automated vehicle disengagement. However, contributing factors and the mechanism about automated vehicle-initiated disengagement has not been quantitatively and comprehensively explored and investigated due to the lack of field test data. Besides, understanding human drivers' perception and promptness of reaction to the AV disengagement is essential to ensure safety transition between automated and manual driving. By harnessing California's Autonomous Vehicle Disengagement Report Database, which includes the AV disengagement data from field tests in 2016–2017, this paper quantitatively investigated the AV disengagement using multiple statistical modeling approaches that involve statistical modeling and classification tree. Specifically, the paper identifies the contributing factors impacting human drivers' promptness to AV disengagements, and quantitatively investigates the underlying causes to AV disengagements. Results indicate that current AV disengagement on public roads is dominated by causes due to a planning issue. The cause of an AV disengagement is significantly induced by lacking certain numbers of radar and LiDAR sensors installed on the automated vehicles. These thresholds of these sensors needed are revealed. Cause of disengagement and roadway characteristics significantly impact drivers' take-over time when facing an AV disengagement. AV perception or control issue-based disengagement can significantly extend drivers' perception-reaction time to take over the driving. The quantitative knowledge obtained ultimately facilitates revealing the mechanisms of the automated vehicle disengagements to ensure safe AV operations on public roads. [ABSTRACT FROM AUTHOR]

By 2020, the EU road transport sector is poised to deploy a host of advanced Intelligent Transport Systems (ITS), including connected and autonomous vehicles (AVs), that are expected to significantly ‘disrupt’ the automotive sector. This vision of the future is fuelling a virtual ‘arms race’ among automakers (OEMs), who are forging unconventional alliances and investing heavily in R&D, in anticipation of a radically changed industry. Car travel seems set to undergo a paradigm shift, evolving from a privately-owned asset into mobility as a service; a metamorphosis that will have significant implications for policymakers and industry stakeholders alike. This paper therefore seeks to address existing gaps in knowledge, by using primary qualitative interview data from industry experts and policymakers to examine the early-stages of the AV transition within the EU automotive industry. This paper also assesses the major policy challenges that face industry regulators tasked with underwriting this radical and dynamic transition to autonomous driving. This paper's focus is on the sociotechnical transition to AVs, which contributes to better understandings about the future role and regulation of Intelligent Transport Systems in society. [ABSTRACT FROM AUTHOR]

The way we drive, and the transport of today are going through radical changes. Intelligent mobility envisions to improve the efficiency of traditional transportation through advanced digital technologies, such as robotics, artificial intelligence and Internet of Things. Central to the development of intelligent mobility technology is the emergence of connected autonomous vehicles (CAVs) where vehicles are capable of navigating environments autonomously. For this to be achieved, autonomous vehicles must be safe, trusted by passengers, and other drivers. However, it is practically impossible to train autonomous vehicles with all the possible traffic conditions that they may encounter. The work in this paper presents an alternative solution of using infrastructure to aid CAVs to learn driving policies, specifically for complex junctions, which require local experience and knowledge to handle. The proposal is to learn safe driving policies through data-driven imitation learning of human-driven vehicles at a junction utilizing data captured from surveillance devices about vehicle movements at the junction. The proposed framework is demonstrated by processing video datasets captured from uncrewed aerial vehicles (UAVs) from three intersections around Europe which contain vehicle trajectories. An imitation learning algorithm based on long short-term memory (LSTM) neural network is proposed to learn and predict safe trajectories of vehicles. The proposed framework can be used for many purposes in intelligent mobility, such as augmenting the intelligent control algorithms in driverless vehicles, benchmarking driver behavior for insurance purposes, and for providing insights to city planning. [ABSTRACT FROM AUTHOR]

The scientific paper presents a practical research carried out by the authors in order to implement certain concepts of technical nature, so that those interested in this field can become aware of a certain way of exposing the problems regarding the role of technology and autonomous vehicles in the sustainable development of car transportation. According to previous research, it has been found that human error has a share of about 95% in the occurrence of road accidents in Europe, causing thousands of human lives to be taken away annually. Experts in the field say that autonomous vehicles can significantly reduce this percentage and at the same time contribute to increasing road safety. Digital technology also has its advantages and disadvantages. It helps reduce congestion and pollution in road traffic, increases access to mobility for people with disabilities, creates new jobs, brings economic growth, increases safety in road traffic but can also become dangerous when disturbances and malfunctions make their mark on artificial intelligence. We present further a technical and managerial study with engineering nuances, through which readers can look at and understand the concept of sustainable development in a global context, presenting the place, role and current stage of development of the autonomous vehicle in the road transport system. At the end, some conclusions and further research directions in the approached field are presented. [ABSTRACT FROM AUTHOR]

Mobility needs, expectations, and concerns vary across age groups and are closely linked to users' views on the future of the road transport system. Automated vehicles are expected to have a significant impact on the future of the road transport system, and pilot deployments are increasingly being tested in Europe and beyond, which is also thanks to the evolving regulatory landscape. As a result, several studies have started to analyse citizens' attitudes towards this technology. However, very few studies have focused on teenagers' views on automated vehicles, although today's children and teenagers could be among the first users of such vehicles. Studying teenagers and the way they envisage automated vehicles in the future is of particular significance in defining transport planning strategies and supporting upcoming policy orientations. To cover this gap, the present study aims to explore teenagers' views about automated vehicles and whether and how they could fit into their future transport setting. A series of on-line and face-to-face focus groups, a demonstration of an automated vehicle prototype, supporting engagement activities, and a post-pre survey were used to collect their views on the topic. The results show that even though the teenagers acknowledged the potential advantages, they also expressed concerns in relation to the interactions with other road users, to automated driving systems' reliability, to safety, and to data privacy. In particular, these safety concerns revealed an unwillingness on the part of the teenagers to be among the first users of automated vehicles. [ABSTRACT FROM AUTHOR]

The vision of cooperative, connected and automated mobility (CCAM) across Europe can only be realized when harmonized solutions that support cross-border traffic exist. The possibility of providing CCAM services along different countries when vehicles drive across various national borders has a huge innovative business potential. However, the seamless provision of connectivity and the uninterrupted delivery of services along borders also poses interesting technical challenges. The situation is particularly innovative given the multi-country, multi-operator, multi-telco-vendor, and multi-car-manufacturer scenario of any cross-border layout. This paper introduces the challenges associated to a cross-border deployment of communication technologies through the analysis of three use cases: tele-operated driving, high-definition map generation and distribution for autonomous vehicles, and anticipated cooperative collision avoidance. Furthermore, a set of 5G solutions have been identified to ensure that CCAM services can be supported efficiently in cross-border scenarios. Faster handover of a data connection from one operator to another, generalized inter-mobile edge computing (MEC) coordination, and quality of service (QoS) prediction are some of the solutions that have been introduced to reduce the uncertainties of a real 5G cross-border deployment. [ABSTRACT FROM AUTHOR]

A general evolution of airspace and air traffic management is ongoing in Europe including significant novelties for manned aviation. The most important one is the full integration of Remotely Piloted Aircraft Systems (RPAS) in the next future and the full integration of automated air vehicles and Driverless Personal Air Vehicles (DPAV) afterwards with manned aircraft. The economic impact of integration of RPAS with manned aircraft into not segregated airspace to perform aerial work is very important. European and worldwide Aviation Authorities and other organizations (EUROCONTROL, JARUS, RTCA, etc.) are working to elaborate new regulations and standards to maintain the current level of safety of aerial operations facing the incoming new entry in the airspace. The implementation of a comprehensive preliminary risk model is deemed to be propaedeutic to any former safety analysis of the new operational scenario: RPAS and manned aircraft flying together into a not segregated airspace. Such risk model is defined in accordance to the Safety Risk Management Pillar of Safety Management System model. The safety analysis for the implementation of the above mentioned preliminary risk model is presented and discussed in this article. [ABSTRACT FROM AUTHOR]

The automotive industry is one of the most important industries in Europe. This industry is responsible for 14% of total production and capital investment in the European manufacturing sector, and in Germany the share amounts to 22%. Structural change within this crucial sector is of relevance for the whole economy. Along with alternative propulsion systems such as electromobility, (semi-)autonomous vehicles are one of the big trends in the automotive industry. For established providers, the possibility of disruptive change poses a threat to existing markets, while a gradual introduction of new technologies also increases competition. Nevertheless, with their strong market position in the premium segment and especially their relevant research activities, European and especially German automotive manufacturers are well placed to successfully master the coming challenges in the automotive market. [ABSTRACT FROM AUTHOR]

The author discusses the applications of unmanned aerial systems (UAS) for civil use which have been developed for security purposes. The author says that Single European Sky ATM Research (SESAR) project aims to develop a new generation air traffic management (ATM) system capable of ensuring the fluidity and safety of the European air transport. She mentions that the UASs are divided by the European legislation with a maximum take-off mass of 150 kilogram (kg) and less than 150 kg.