Autonomous Vehicles for Smart and Sustainable Cities: An In-Depth Exploration of Privacy and Cybersecurity Implications

A Microscopic Platoon Stability Model Using Vehicle-to-Vehicle Communication

With Vehicle-to-Vehicle (V2V) communication capability, vehicle platoon on the highway helps to reduce traffic congestion. However, the dynamic nature of vehicles imposes challenges on the V2V-based platoon management. In this paper, by considering the characteristics of a Vehicular Ad-hoc Network (VANET), a microscopic platoon management scheme is proposed to deal with three basic dynamic platoon maneuvers, namely merging, splitting, and speed-change. The congestion detection feature of VANET is used as a scale for platoon merging, splitting, and speed selection. Real-time congestion is detected if the number of vehicles in a given road segment exceeds the occupancy rate or the time headway is less than the thresholds. In the proposed platoon management scheme, platoon maintenance is triggered in congestion detection. Finally, a VANET-based platoon platform is built by using Network Simulator Version 2 (NS2) network simulation to assess the performance over some real road traces generated by Simulation of Urban MObility (SUMO). It is shown that V2V-based dynamic vehicle platoon management provides an inexpensive technique to cope with the dynamic platoon management requirement.

The Interface of Privacy and Data Security in Automated City Shuttles: The GDPR Analysis

The fast evolution and prevalence of driverless technologies has facilitated the testing and deployment of automated city shuttles (ACSs) as a means of public transportation in smart cities. For their efficient functioning, ACSs require a real-time data compilation and exchange of information with their internal components and external environment. However, that nexus of data exchange comes with privacy concerns and data protection challenges. In particular, the technical realization of stringent data protection laws on data collection and processing are key issues to be tackled within the ACSs ecosystem. Our work provides an in-depth analysis of the GDPR requirements that should be considered by the ACSs’ stakeholders during the collection, storage, use, and transmission of data to and from the vehicles. First, an analysis is performed on the data processing principles, the rights of data subjects, and the subsequent obligations for the data controllers where we highlight the mixed roles that can be assigned to the ACSs stakeholders. Secondly, the compatibility of privacy laws with security technologies focusing on the gap between the legal definitions and the technological implementation of privacy-preserving techniques are discussed. In face of the GDPR pitfalls, our work recommends a further strengthening of the data protection law. The interdisciplinary approach will ensure that the overlapping stakeholder roles and the blurring implementation of data privacy-preserving techniques within the ACSs landscape are efficiently addressed.

Privacy-Preserved In-Cabin Monitoring System for Autonomous Vehicles

Fully autonomous vehicles (FAVs) lack monitoring inside the cabin. Therefore, an in-cabin monitoring system (IMS) is required for surveilling people causing irregular or abnormal situations. However, monitoring in the public domain allows disclosure of an individual’s face, which goes against privacy preservation. Furthermore, there is a contrary demand for privacy in the IMS of AVs. Therefore, an intelligent IMS must simultaneously satisfy the contrary requirements of personal privacy protection and person identification during abnormal situations. In this study, we proposed a privacy-preserved IMS, which can reidentify anonymized virtual individual faces in an abnormal situation. This IMS includes a step for extracting facial features, which is accomplished by the edge device (onboard unit) of the AV. This device anonymizes an individual’s facial identity before transmitting the video frames to a data server. We created different abnormal scenarios in the vehicle cabin. Further, we reidentified the involved person by using the anonymized virtual face and the reserved feature vectors extracted from the suspected individual. Overall, the proposed approach preserves personal privacy while maintaining security in surveillance systems, such as for in-cabin monitoring of FAVs.

Distributed ledger technologies in vehicular mobile edge computing: a survey

Blockchain-based systems, coined by distributed ledger technologies (DLTs), have rapidly received tremendous interest from academia, industries, and governments. Recent literature has revealed many research and developments on applying DLTs to the Internet of things (IoT), cloud-edge computing. In this survey, we conduct a comprehensive survey of the newly appeared concepts, theories, platforms, and DLTs-facilitated applications of vehicular networks and mobile edge computing (MEC). We also review the selections of the available DLTs related platforms and tools. Future research directions and issues are discussed, including security, privacy, scalability issues, and multiple applications in various domains.

Integration of Blockchain with Connected and Autonomous Vehicles: Vision and Challenge

Connected and Autonomous Vehicles (CAVs) are introduced to improve individuals’ quality of life by offering a wide range of services. They collect a huge amount of data and exchange them with each other and the infrastructure. The collected data usually includes sensitive information about the users and the surrounding environment. Therefore, data security and privacy are among the main challenges in this industry. Blockchain, an emerging distributed ledger, has been considered by the research community as a potential solution for enhancing data security, integrity, and transparency in Intelligent Transportation Systems (ITS). However, despite the emphasis of governments on the transparency of personal data protection practices, CAV stakeholders have not been successful in communicating appropriate information with the end users regarding the procedure of collecting, storing, and processing their personal data, as well as the data ownership. This article provides a vision of the opportunities and challenges of adopting blockchain in ITS from the “data transparency” and “privacy” perspective. The main aim is to answer the following questions: (1) Considering the amount of personal data collected by the CAVs, such as location, how would the integration of blockchain technology affect transparency , fairness , and lawfulness of personal data processing concerning the data subjects (as this is one of the main principles in the existing data protection regulations)? (2) How can the trade-off between transparency and privacy be addressed in blockchain-based ITS use cases?

The assessment of smart city information security risk in China based on zGT2FSs and IAA method

The continuous expansion of the construction scale of smart city has reconstructed the urban information pattern. How to maintain the stability of information security while giving full play to the role of information sharing is a practical problem that must be solved for the sustainable development of smart city. Based on the information ecology theory, this paper construct the smart city information security risk evaluation system from six aspects. Then, zGT2FSs is established based on type-2 fuzzy set theory and IAA method, which fully considers the internal and external uncertainty of expert decision-making. According to the calculation results, the key influencing factors of information security risk of smart city are analyzed to provide suggestions and guidance for the formulation of information security control in the process of smart city construction in China.

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

The rapid expansion of a country's economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system's IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system's infrastructure is far too low-cost and easy to install.

Diversity-Sensitive Generative Adversarial Network for Terrain Mapping Under Limited Human Intervention

In a collaborative air-ground robotic system, the large-scale terrain mapping using aerial images is important for the ground robot to plan a globally optimal path. However, it is a challenging task in a novel and dynamic field without historical human supervision. To alleviate the reliance on human intervention, this article presents a novel framework that integrates active learning and generative adversarial networks (GANs) to effectively exploit small human-labeled data for terrain mapping. In order to model the diverse terrain patterns, this article designs two novel diversity-sensitive GAN models which can capture fine-grained terrain classes among aerial image patches. The proposed approaches are tested in two real-world scenarios using our collaborative air-ground robotic platform. The empirical results show that our methods can outperform their counterparts in the predictive accuracy of terrain classification, visual quality of terrain mapping, and average length of the planned ground path. In practice, the proposed terrain mapping framework is especially valuable when the budget in time or labor cost is very limited.

Connected and Autonomous Vehicles and Infrastructures: A Literature Review

The continuous integration of advanced driver-assist systems (ADAS) into connected and autonomous vehicles (CAV) is accelerating the transition of human-driven vehicles to a fully driverless option. The most available ADASs in current running vehicles with their functions, level of autonomy, and present scenario of CAV deployment are critically reviewed and summarized in this paper. The expected advantages with the probable and observed uncertainties of the CAV deployment are also presented. The technological accomplishments (advanced digital infrastructures along with other technologies) are incorporating rapid improvement in vehicle automation. This paper reviews the engagement of technologies in the functioning of automated vehicles and their implementation challenges. Physical infrastructures facilitate the features of automation and connectivity in moving on the roads. Various roadside infrastructures with the issues in assisting the CAV for path tracking are summarized in this paper. The reduction of lane width due to lane-keeping ADAS and replacement of human drivers (i.e., eye) by machine impacts the various geometric elements of highways. The reduced lane width and truck platooning from the integration of cooperative adaptive cruise control affect the structural performance of the pavements significantly. This paper addresses the effects of connected and automated driving on geometric elements and the structural performance of highways. In addition, various techniques to minimize the additional distresses of connected and automated driving are also explained in this paper.

Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in China

Smart cities play a vital role in the growth of a nation. In recent years, several countries have made huge investments in developing smart cities to offer sustainable living. However, there are some challenges to overcome in smart city development, such as traffic and transportation management, energy and water distribution and management, air quality and waste management monitoring, etc. The capabilities of the Internet of Things (IoT) and artificial intelligence (AI) can help to achieve some goals of smart cities, and there are proven examples from some cities like Singapore, Copenhagen, etc. However, the adoption of AI and the IoT in developing countries has some challenges. The analysis of challenges hindering the adoption of AI and the IoT are very limited. This study aims to fill this research gap by analyzing the causal relationships among the challenges in smart city development, and contains several parts that conclude the previous scholars’ work, as well as independent research and investigation, such as data collection and analysis based on DEMATEL. In this paper, we have reviewed the literature to extract key challenges for the adoption of AI and the IoT. These helped us to proceed with the investigation and analyze the adoption status. Therefore, using the PRISMA method, 10 challenges were identified from the literature review. Subsequently, determination of the causal inter-relationships among the key challenges based on expert opinions using DEMATEL is performed. This study explored the driving and dependent power of the challenges, and causal relationships between the barriers were established. The results of the study indicated that “lack of infrastructure (C1)”, ”insufficient funds (C2)”, “cybersecurity risks (C3)”, and “lack of trust in AI, IoT” are the causal factors that are slowing down the adoption of AI and IoT in smart city development. The inter-relationships between the various challenges are presented using a network relationship map, cause–effect diagram. The study’s findings can help regulatory bodies, policymakers, and researchers to make better decisions to overcome the challenges for developing sustainable smart cities.

Ethical dilemmas are really important to potential adopters of autonomous vehicles

The ethical dilemma (ED) of whether autonomous vehicles (AVs) should protect the passengers or pedestrians when harm is unavoidable has been widely researched and debated. Several behavioral scientists have sought public opinion on this issue, based on the premise that EDs are critical to resolve for AV adoption. However, many scholars and industry participants have downplayed the importance of these edge cases. Policy makers also advocate a focus on higher level ethical principles rather than on a specific solution to EDs. But conspicuously absent from this debate is the view of the consumers or potential adopters, who will be instrumental to the success of AVs. The current research investigated this issue both from a theoretical standpoint and through empirical research. The literature on innovation adoption and risk perception suggests that EDs will be heavily weighted by potential adopters of AVs. Two studies conducted with a broad sample of consumers verified this assertion. The results from these studies showed that people associated EDs with the highest risk and considered EDs as the most important issue to address as compared to the other technical, legal and ethical issues facing AVs. As such, EDs need to be addressed to ensure robustness in the design of AVs and to assure consumers of the safety of this promising technology. Some preliminary evidence is provided about interventions to resolve the social dilemma in EDs and about the ethical preferences of prospective early adopters of AVs.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10676-021-09605-y.

Optimization-Driven Powertrain-Oriented Adaptive Cruise Control to Improve Energy Saving and Passenger Comfort

Assessing the potential of advanced driver assistance systems requires developing dedicated control algorithms for controlling the longitudinal speed of automated vehicles over time. In this paper, a multiobjective off-line optimal control approach for planning the speed of the following vehicle in adaptive cruise control (ACC) driving is proposed. The implemented method relies on the principle of global optimality fostered by dynamic programming (DP) and aims to minimize propelling energy consumption and enhance passenger comfort. The powertrain model and onboard control system are integrated within the proposed car-following optimization framework. The retained ACC approach ensures that the distance between the following vehicle and the preceding vehicle is always maintained within allowed limits. The flexibility of the proposed method is demonstrated here through ease of implementation on a wide range of powertrain categories, including a conventional vehicle propelled by an internal combustion engine solely, a pure electric vehicle, a parallel P2 hybrid electric vehicle (HEV) and a power-split HEV. Moreover, different driving conditions are considered to prove the effectiveness of the proposed optimization-driven ACC approach. Obtained simulation results suggest that up to 22% energy-saving and 48% passenger comfort improvement might be achieved for the ACC-enabled vehicle compared with the preceding vehicle by implementing the proposed optimization-driven ACC approach. Engineers may adopt the proposed workflow to evaluate corresponding real-time ACC approaches and assess optimal powertrain design solutions for ACC driving.

Smart Mobility: The Main Drivers for Increasing the Intelligence of Urban Mobility

Urban mobility plays a key role in the ecosystems of complex smart cities. It is considered a key factor in enabling cities to become more intelligent, which highlights the importance of identifying the drivers that improve the intelligence of cities. In this study, we investigate the main drivers with the potential to increase urban mobility intelligence and assign them a priority. Following on from a systematic review of the literature, we conducted broad and detailed bibliographic research based on the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). We also surveyed 181 professionals working in the field concerned to confirm the importance of different drivers and assign them a level of priority. The results show that 27 drivers identified in the literature were considered important, of which seven, related to city governance and technical solutions, were considered the most important to increase urban mobility intelligence.

Cyber-attacks in the next-generation cars, mitigation techniques, anticipated readiness and future directions

Modern-day Connected and Autonomous Vehicles (CAVs) with more than 100 million code lines, running up-to a hundred Electronic Control Units (ECUs) will create and exchange digital information with other vehicles and intelligent transport networks. Consequently, ubiquitous internal and external communication (controls, commands, and data) within all CAV-related nodes is inevitably the gatekeeper for the smooth operation. Therefore, it is a primary vulnerable area for cyber-attacks that entails stringent and efficient measures in the form of "cybersecurity". There is a lack of systematic and comprehensive review of the literature on cyber-attacks on the CAVs, respective mitigation strategies, anticipated readiness, and research directions for the future. This study aims to analyse, synthesise, and interpret critical areas for the roll-out and progression of CAVs in combating cyber-attacks. Specifically, we described in a structured way a holistic view of potentially critical avenues, which lies at the heart of CAV cybersecurity research. We synthesise their scope with a particular focus on ensuring effective CAVs deployment and reducing the probability of cyber-attack failures. We present the CAVs communication framework in an integrated form, i.e., from In-Vehicle (IV) communication to Vehicle-to-Vehicle (V2X) communication with a visual flowchart to provide a transparent picture of all the interfaces for potential cyber-attacks. The vulnerability of CAVs by proximity (or physical) access to cyber-attacks is outlined with future recommendations. There is a detailed description of why the orthodox cybersecurity approaches in Cyber-Physical System (CPS) are not adequate to counter cyber-attacks on the CAVs. Further, we synthesised a table with consolidated details of the cyber-attacks on the CAVs, the respective CAV communication system, its impact, and the corresponding mitigation strategies. It is believed that the literature discussed, and the findings reached in this paper are of great value to CAV researchers, technology developers, and decision-makers in shaping and developing a robust CAV-cybersecurity framework.

The First Two Decades of Smart City Research from a Risk Perspective

Although they offer major advantages, smart cities present unprecedented risks and challenges. There are abundant discrete studies on risks related to smart cities; however, such risks have not been thoroughly understood to date. This paper is a systematic review that aims to identify the origin, trends, and categories of risks from previous studies on smart cities. This review includes 85 related articles published between 2000 and 2019. Through a thematic analysis, smart city risks were categorized into three main themes: organizational, social, and technological. The risks within the intersections of these themes were also grouped into (1) digital transformation, (2) socio-technical, and (3) corporate social responsibility. The results revealed that risk is a comparatively new topic in smart-city research and that little focus has been given to social risks. The findings indicated that studies from countries with a long history of smart cities tend to place greater emphasis on social risks. This study highlights the significance of smart city risks for researchers and practitioners, providing a solid direction for future smart-city research.

How Can Smart Mobility Innovations Alleviate Transportation Disadvantage? Assembling a Conceptual Framework through a Systematic Review

Transportation disadvantage is about the difficulty accessing mobility services required to complete activities associated with employment, shopping, business, essential needs, and recreation. Technological innovations in the field of smart mobility have been identified as a potential solution to help individuals overcome issues associated with transportation disadvantage. This paper aims to provide a consolidated understanding on how smart mobility innovations can contribute to alleviate transportation disadvantage. A systematic literature review is completed, and a conceptual framework is developed to provide the required information to address transportation disadvantage. The results are categorized under the physical, economic, spatial, temporal, psychological, information, and institutional dimensions of transportation disadvantage. The study findings reveal that: (a) Primary smart mobility innovations identified in the literature are demand responsive transportation, shared transportation, intelligent transportation systems, electric mobility, autonomous vehicles, and Mobility-as-a-Services. (b) Smart mobility innovations could benefit urban areas by improving accessibility, efficiency, coverage, flexibility, safety, and the overall integration of the transportation system. (c) Smart mobility innovations have the potential to contribute to the alleviation of transportation disadvantage. (d) Mobility-as-a-Service has high potential to alleviate transportation disadvantage primarily due to its ability to integrate a wide-range of services.

Scientific Landscape of Smart and Sustainable Cities Literature: A Bibliometric Analysis

The smart sustainable city (SSC) is a concept created in response to problems and challenges arising from rapid urbanization. This is a relatively new term that is developing dynamically, which is confirmed by the growing number of publications over recent years. For this reason, this article presented an up-to-date comprehensive bibliometric analysis to describe and assess the scientific landscape of smart and sustainable cities literature. The analysis was based on two bibliographic sources—the Web of Science Core Collection and the Scopus database. It covers publications on the SSC, as well as documents describing the smart city (SC) and the sustainable city (SuC) concepts separately. VOSviewer and Biblioshiny were selected as software tools for the bibliometric analysis. Based on the descriptive bibliometric analysis, quantity and quality indicators were determined separately for the SC, SuC, and SSC concepts, while the network analysis mapped and covered the level of multi-faceted scientific cooperation in the field of the SSC research. The analysis results were intended to familiarize scholars and practitioners with the most prolific authors, sources, institutions, and countries in the analyzed scientific field, to identify the most influential research channels and impact from authors, sources, countries, and research topics, to determine major clusters of the SSC research and also to provide valuable information for further investigation.

Algorithmic Decision-Making in AVs: Understanding Ethical and Technical Concerns for Smart Cities

Autonomous Vehicles (AVs) are increasingly embraced around the world to advance smart mobility and more broadly, smart, and sustainable cities. Algorithms form the basis of decision-making in AVs, allowing them to perform driving tasks autonomously, efficiently, and more safely than human drivers and offering various economic, social, and environmental benefits. However, algorithmic decision-making in AVs can also introduce new issues that create new safety risks and perpetuate discrimination. We identify bias, ethics, and perverse incentives as key ethical issues in the AV algorithms’ decision-making that can create new safety risks and discriminatory outcomes. Technical issues in the AVs’ perception, decision-making and control algorithms, limitations of existing AV testing and verification methods, and cybersecurity vulnerabilities can also undermine the performance of the AV system. This article investigates the ethical and technical concerns surrounding algorithmic decision-making in AVs by exploring how driving decisions can perpetuate discrimination and create new safety risks for the public. We discuss steps taken to address these issues, highlight the existing research gaps and the need to mitigate these issues through the design of AV’s algorithms and of policies and regulations to fully realise AVs’ benefits for smart and sustainable cities.

Human Factors in the Cybersecurity of Autonomous Vehicles: Trends in Current Research

The cybersecurity of autonomous vehicles (AVs) is an important emerging area of research in traffic safety. Because human failure is the most common reason for a successful cyberattack, human-factor researchers and psychologists might improve AV cybersecurity by researching how to decrease the probability of a successful attack. We review some areas of research connected to the human factor in cybersecurity and find many potential issues. Psychologists might research the characteristics of people prone to cybersecurity failure, the types of scenarios they fail in and the factors that influence this failure or over-trust of AV. Human behavior during a cyberattack might be researched, as well as how to educate people about cybersecurity. Multitasking has an effect on the ability to defend against a cyberattack and research is needed to set the appropriate policy. Human-resource researchers might investigate the skills required for personnel working in AV cybersecurity and how to detect potential defectors early. The psychological profile of cyber attackers should be investigated to be able to set policies to decrease their motivation. Finally, the decrease of driver's driving skills as a result of using AV and its connection to cybersecurity skills is also worth of research.

A Fuzzy WASPAS-Based Approach to Determine Critical Information Infrastructures of EU Sustainable Development

Critical information infrastructure exists in different sectors of each country. Its loss or sustainability violation will lead to a negative impact on the supply of essential services, as well as on the social or economic well-being of the population. It also may even pose a threat to people’s health and lives. In the modern world, such infrastructure is more vulnerable and unstable than ever, due to rapid technological changes, and the emergence of a new type of threat—information threats. It is necessary to determine which infrastructure are of crucial importance when decision-makers aim to achieve the reliability of essential infrastructure. This article aims to solve the problem of ensuring the sustainable development of EU countries in terms of identifying critical information infrastructures. Integrated multi-criteria decision-making techniques based on fuzzy WASPAS and AHP methods are used to identify essential information infrastructures, which are related to a new type of potential threat to national security. The paper proposes a model for identifying critical information infrastructures, taking into account the sustainable development of countries.

SUSTAINABLE DEVELOPMENT OF TRANSPORTATION IN THE CITIES OF RUSSIA: EXPERIENCE AND PRIORITIES

Aim.The aim is to reveal the need and relevant directions for the implementation of the concept of sustainable development in relation to the efficiency of transport and logistics activities.Methods.The methodology of research and development includes the analysis and adaptation of the experience in the areas of sustainable urban development and their transport, environmental impact of transport, planning and organizing passenger transport in smart cities, theories of utility and efficiency. In order to analyze the state of the investigated problem, modern scientific publications of Russian and foreign scientists were used.Results.We considered the modern understanding of the concepts of sustainable development and smart cities, the globalization of their use by the executive authorities in developed countries to improve the economy and quality of life. Were also indicated the main problems of the transition to sustainable development, characteristic of Russia. Various examples of the implementation of the concepts of sustainable development and smart city, the use of modern information technologies and methods for evaluating transport and logistics projects were given. The environmental consequences of using the concept of sustainable development, as well as the need to transform the land use policies in cities were indicated. The approaches to assessing the efficiency of transport and logistics systems and their development projects were considered. The directions of improving the human resourcing for sustainable development were identified.Conclusions.The main directions of succeeding in sustainable urban development and implementation of projects for the development of the transport systems were established. Recommendations were formulated to ensure the planning of sustainable development and building of a methodology to evaluate the efficiency of transportation and logistics as well as the projects for their improvement.