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In the field of Cooperative Intelligent Transport Systems (C-ITSs), the traditional approach to testing often emphasizes technological parameters, leaving the validation of message content insufficiently addressed. Since the content of these messages is crucial for the correct functioning of C-ITS, this article demonstrates the potential for automated evaluation of C-ITS message content against relevant standards. It leverages our novel tools, Karlos and C-ITS SIM, to facilitate this process. Through detailed laboratory testing and data analysis, the study showcases the effectiveness of these automated solutions in enhancing the accuracy and reliability of message content validation. [ABSTRACT FROM AUTHOR]

In the dynamic realm of autonomous mobility, understanding legal protection for databases is crucial. The upcoming article focuses on key aspects in this area. It clarifies fundamental concepts, including both sui generis and copyright safeguards. Additionally, it outlines important characteristics for assessing potential legal protection. The article also introduces a unique set of coefficients, making complex legal concepts more tangible. This innovative approach offers makers concrete insights into their database's level of legal protection. By combining theoretical understanding with practical application, the article equips makers in the autonomous mobility sector to implement effective strategies for securing legal protection. Furthermore, the article will delve into simplified typologies of databases within autonomous systems. It will also provide a step-bystep illustration of how to determine the degree of legal protection for these databases. This process will include supplementary information aimed at refining the assessment of legal protection, offering makers the necessary guidance for accurate decisions. Through this comprehensive approach, the article aims to provide makers with practical tools to navigate the complex landscape of database legal protection efficiently. [ABSTRACT FROM AUTHOR]

This paper introduces the issue of personal and non-personal data in autonomous vehicles. Its main objective is to provide a procedure for determining the processed and retained data, to explain the current legal regulation that must be met, and to propose appropriate recommendations for each data category. Current law (and the regulatory framework which it has produced) generally distinguishes between two basic types of data, i.e. non-personal data and personal data. The phenomenon that a combination of several pieces of non-personal data may, under certain circumstances2, result in processing of personal data, which is subject to significant regulation, is not limited solely to the field of autonomous mobility. This paper deals with data classification in autonomous vehicles, focusing on the challenging cases of non-personal data that can turn into personal data. The initial stage involves identifying the data to be processed, followed by the classification of the identified data. Based on this classification, relevant legal obligations and recommendations are described. The primary objective of this article is to provide guidance to data administrators (or, to use the terminology of data protection laws, data controllers) in autonomous mobility, enabling them to identify data and take appropriate measures to ensure compliance with legal regulations. The ultimate aim is to ensure that data controllers adhere to legal requirements, a goal that is critical for all data controllers. Beyond that, this article should be relevant also for other stakeholders in the autonomous mobility ecosystem, such as vehicle manufacturers and their suppliers, software application and service providers, and others. [ABSTRACT FROM AUTHOR]

The article presents a novel algorithm for merging Bayesian networks generated by different methods, such as expert knowledge and data-driven approaches, while leveraging a symmetry-based approach. The algorithm combines the strengths of each input network to create a more comprehensive and accurate network. Evaluations on traffic accident data from Prague in the Czech Republic and accidents on railway crossings demonstrate superior predictive performance, as measured by prediction error metric. The algorithm identifies and incorporates symmetric nodes into the final network, ensuring consistent representations across different methods. The merged network, incorporating nodes selected from both the expert and algorithm networks, provides a more comprehensive and accurate representation of the relationships among variables in the dataset. Future research could focus on extending the algorithm to deal with cycles and improving the handling of conditional probability tables. Overall, the proposed algorithm demonstrates the effectiveness of combining different sources of knowledge in Bayesian network modeling. [ABSTRACT FROM AUTHOR]

In the area of smart cities, great emphasis is placed on many different fields such as energetics, information systems, and transportation. All of these should lead to a simplification of life thanks to smart technologies. If we talk about the transportation field, the main issues related to this area are safety, traffic efficiency, or the environment. Another condition is the successful acceptance of any new technology by its users. Cooperative systems prove to be a suitable solution for these issues, especially in urban areas. Today, pilot implementations of cooperative systems in European countries are being carried out. However, before they are put into full operation, they need to be tested, evaluated, and assessed. This article focuses on the latter two points, i.e., evaluation and assessment of the cooperative systems. For this purpose, a methodology was created, which describes the procedure chosen in the evaluation and assessment of cooperative systems in the Czech Republic and a demonstration of its use by example. The methodology is focused on three main areas, which in this case are functional evaluation, user acceptance, and impact assessment. For the area of user acceptance, the main source was questionnaires, impact assessment relied on measured data while functional evaluation was based on discussions with the drivers, evaluating the cooperative systems, the measured data, and the expert observations. All collected and measured data were then processed and some of the results of the evaluation of the selected service are presented at the end of this article. [ABSTRACT FROM AUTHOR]

Objective: To assess the plausibility of using the continuous glucose monitoring as a sole source of data for the screening of the neonatal hypoglycemia.Study design: Infants of mothers with diabetes were screened for neonatal hypoglycemia (less than 2.5 mmol/l after 4 h of life). Initial measurement was performed using point of care analyzer. We applied continuous glucose monitoring system subsequently. Infants were monitored up to 5 days or until discharge.Results: Out of 32 infants 11 had postnatal hypoglycemia resolved within 12 h of life. Two infants had hypoglycemia found due to continuous glucose monitoring after 24 h of life when sufficient feeding was established and they did not show any signs of hypoglycemia. We did not have any false negative measurements. No infant showed clinical signs of neonatal hypoglycemia.Conclusions: Continuous glucose monitoring is plausible and safe to use for screening of neonatal hypoglycemia. It operates well within the range that is accepted as safe for neurodevelopment. In addition, it can be used after first day of life where regular screening ends. Limitation of this method is possible alarm negligence of caregivers. [ABSTRACT FROM AUTHOR]