A conceptual system dynamics model for cybersecurity assessment of connected and autonomous vehicles

Modelling cybersecurity regulations for automated vehicles

Technological advancements in Connected and Automated Vehicles (CAVs), particularly the integration of diverse stakeholder groups (communication service providers, road operators, automakers, repairers, CAV consumers, and the general public) and the pursuit of new economic opportunities, have resulted in the emergence of new technical, legal, and social challenges. The most pressing challenge is deterring criminal behaviour in both the physical and cyber realms through the adoption of CAV cybersecurity protocols and regulations. However, the literature lacks a systematic decision tool to analyze the impact of the potential cybersecurity regulations for dynamically interacting stakeholders, and to identify the leverage points to minimise the cyber-risks. To address this knowledge gap, this study uses systems theory to develop a dynamic modelling tool to analyze the indirect consequences of potential CAVs cybersecurity regulations in the medium to long term. It is hypothesized that CAVs Cybersecurity Regulatory Framework (CRF) is the property of the entire ITS stakeholders. The CRF is modelled using the System Dynamic based Stock-and-Flow-Model (SFM) technique. The SFM is founded on five critical pillars: the Cybersecurity Policy Stack, the Hacker's Capability, Logfiles, CAV Adopters, and intelligence-assisted traffic police. It is found that decision-makers should focus on three major leverage points: establishing a CRF grounded on automakers' innovation; sharing risks in eliminating negative externalities associated with underinvestment and knowledge asymmetries in cybersecurity; and capitalising on massive CAV-generated data in CAV operations. The formal integration of intelligence analysts and computer crime investigators to strengthen traffic police capabilities is pivotal. Recommendations for automakers include data-profiteering in CAV design, production, sales, marketing, safety enhancements and enabling consumer data transparency.Furthermore, CAVs-CRF necessitate a balanced approach to the trade-off between: i) data accessibility constraints on CAV automakers and ITS service providers; ii) regulator command and control thresholds; iii) automakers' business investment protection; and iv) consumers' data privacy guard.

A multinational empirical study of perceived cyber barriers to automated vehicles deployment

The digital transformation of Automated Vehicles (AVs) has raised concerns in the cyber realm among prospective AV consumers. However, there is a dearth of empirical research on how cyber obstacles may impact the operation of AVs. To address this knowledge gap, this study examines the six critical cyber impediments (data privacy, AV connectivity, ITS infrastructure, lack of cybersecurity regulations, AV cybersecurity understanding, and AV cyber-insurance) that influence the deployment of AVs. The impact of gender, age, income level, and individual AV and cybersecurity knowledge on these obstacles are statistically assessed using a sample of 2061 adults from the United States, the United Kingdom, New Zealand, and Australia. The research revealed intriguing empirical findings on all cyber barriers in the form of a trichotomy: participants' education level, understanding of AVs, and cybersecurity knowledge. As education levels increase, the significance of a cyber barrier to AV deployment decreases; however, as AV comprehension and cybersecurity knowledge increase, the perception of a cyber barrier becomes significantly more important. In addition, the study demonstrates differences in perceptions of cyber barriers and AV deployments based on gender, age, income, and geographic location. This study's findings on cyber barriers and AV deployment have implications for academia and industry.

Physical and Digital Infrastructure Readiness Index for Connected and Automated Vehicles

In this paper, we present an assessment framework that can be used to score segments of physical and digital infrastructure based on their features and readiness to expedite the deployment of Connected and Automated Vehicles (CAVs). We discuss the equipment and methodology applied for the collection and analysis of required data to score the infrastructure segments in an automated way. Moreover, we demonstrate how the proposed framework can be applied using data collected on a public transport route in the city of Zilina, Slovakia. We use two types of data to demonstrate the methodology of the assessment-connectivity and positioning data to assess the connectivity and localization performance provided by the infrastructure and image data for road signage detection using a Convolutional Neural Network (CNN). The core of the research is a dataset that can be used for further research work. We collected and analyzed data in two settings-an urban and suburban area. Despite the fact that the connectivity and positioning data were collected in different days and times, we found highly underserved areas along the investigated route. The main problem from the point of view of communication in the investigated area is the latency, which is an issue associated with infrastructure segments mainly located at intersections with heavy traffic or near various points of interest. The low accuracy of localization has been observed mainly in dense areas with large buildings and trees, which decrease the number of visible localization satellites. To address the problem of automated assessment of the traffic sign recognition precision, we proposed a CNN that achieved 99.7% precision.

State-of-the-Art of Factors Affecting the Adoption of Automated Vehicles

Around 90% of accidents stem from human error. Disruptive technology, especially automated vehicles (AVs), can respond to the problems by, for instance, eradicating human error when driving, thus increasing energy efficiency due to the platoon effect, and potentially giving more space to human activities by decreasing parking space; hence, with the introduction of the autonomous vehicle, the public attitude towards its adoption needs to be understood to develop appropriate strategies and policies to leverage the potential benefits. There is a lack of a systematic and comprehensive literature review on adoption attitudes toward AVs that considers various interlinked factors such as road traffic environment changes, AV transition, and policy impacts. This study aims to synthesize past research regarding public acceptance attitude toward AVs. More specifically, the study investigates driverless technology and uncertainty, road traffic environment changes, policy impact, and findings from AV adoption modelling approaches, to understand public attitudes towards AVs. The study points out critical problems and future directions for analysis of AV impacts, such as the uncertainty on AVs adoption experiment, policy implementation and action plans, the uncertainty of AV-related infrastructure, and demand modelling.

Digital Resilience Among Individuals in School Education Settings: A Concept Analysis Based on a Scoping Review

BACKGROUND

Nowadays, in an informational society, digital technologies are present in most areas of life, including school education fields. Students encounter risks or threats during online experiences. Digital resilience helps individuals recognize and manage the risks and threats they come across when they socialize, explore, or work online and plays an important role in the digital technology challenges. However, so far, the concept of digital resilience among individuals in the educational field has not been analyzed in detail.

OBJECTIVES

The purpose of this study is to clarify the concept of digital resilience among students in a school education context, describe antecedents and consequences, and suggest a conceptual model for health educators.

METHODS

Walker and Avants' concept analysis method and standards of the scoping review were used to clarify the attributes, antecedents, and consequences from the included articles. A thematic analysis approach of literature was utilized to describe the study findings. No date limitations were applied.

RESULTS

A total of 22 included articles provided data for digital resilience conceptualization. Five defining attributes for the concept were identified as follows: (1) understanding online threats; (2) knowing solutions; (3) learning knowledge and skills; (4) recovering from stress; and (5) moving forward through self-efficacy. Antecedents included digital technology-related threats influenced by individual external and internal factors. Consequences were divided into two categories: behavioral performance and psychosocial functioning.

CONCLUSION

Based on the results of the concept analysis, a preliminary conceptual model of digital resilience was described as a circular process toward greater performance and function in the form of understanding, knowing, learning, recovering, and moving forward, when facing stressors, challenges, or adversity. The conceptual model of digital resilience can be further tested and may inform the enhancement of digital-specific resilience measures and interventions for students.