Objective: In order to introduce automated vehicles on public roads, it is necessary to ensure that these vehicles are safe to operate in traffic. One challenge is to prove that all physically possible variations of situations can be handled safely within the operational design domain of the vehicle. A promising approach to handling the set of possible situations is to identify a manageable number of logical scenarios, which provide an abstraction for object properties and behavior within the situations. These can then be transferred into concrete scenarios defining all parameters necessary to reproduce the situation in different test environments. Methods: This article proposes a framework for defining safety-relevant scenarios based on the potential collision between the subject vehicle and a challenging object, which forces the subject vehicle to depart from its planned course of action to avoid a collision. This allows defining only safety-relevant scenarios, which can directly be related to accident classification. The first criterion for defining a scenario is the area of the subject vehicle with which the object would collide. As a second criterion, 8 different positions around the subject vehicle are considered. To account for other relevant objects in the scenario, factors that influence the challenge for the subject vehicle can be added to the scenario. These are grouped as action constraints, dynamic occlusions, and causal chains. Results: By applying the proposed systematics, a catalog of base scenarios for a vehicle traveling on controlled-access highways has been generated, which can directly be linked to parameters in accident classification. The catalog serves as a basis for scenario classification within the PEGASUS project. Conclusions: Defining a limited number of safety-relevant scenarios helps to realize a systematic safety assurance process for automated vehicles. Scenarios are defined based on the point of the potential collision of a challenging object with the subject vehicle and its initial position. This approach allows defining scenarios for different environments and different driving states of the subject vehicle using the same mechanisms. A next step is the generation of logical scenarios for other driving states of the subject vehicle and for other traffic environments.

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Šio baigiamojo magistro darbo tikslas yra išanalizuoti keleivių saugumo užtikrinimo būdus Tarptautiniame Vilniaus oro uosto terminale ir pateikti pasiūlymus jo gerinimui. Darbą sudaro dvi pagrindinės dalys. Pirmojoje teorinėje dalyje nagrinėjami oro uostų saugumo reikalavimai tarptautiniu ir Lietuvos mastu. Šioje darbo dalyje taip pat analizuojami oro uostų saugumo ypatybės, terminalų struktūra, nustatomi pavojai ir rizikos oro uostose. Antroje darbo dalyje charakterizuojamas Tarptautinis Vilniaus oro uostas bei jo saugumo organizavimas. Skyrius skirtas analitinei daliai, kurioje numatomos oro uosto keleivių saugumo tobulinimo galimybės. Taikant kompiuterinį modeliavimą apskaičiuojami evakuacijos laikai, suskaičiuojami evakuacijos efektyvumą apibūdinantys rodikliai. Nustatoma geriausias scenarijus. Išsprendus minėtą uždavinį, pateikiamos išvados ir pasiūlymai. / The aim of this master thesis is to analyze the security of passengers at Vilnius International Airport terminal and to make suggestions for improvement. The work consists of two main parts. In the first theoretical part the airport security requirements of international and Lithuanian scale are analyzed. This part also consists of measurement of the characteristics of airport security, terminal structure, identification of hazards and risks at the airport. In the second part Vilnius International Airport characteristics and its security organization are overviewed. This part is devoted to analytical part, which provides airport passenger security improvement. The application of computer simulation is adopted in calculating evacuation time and possible evacuation performance indicators. The best scenario is determined. Having solved that challenge, the conclusions and recommendations are presented.

Les arguments de sécurité sont couramment utilisés pour montrer que des efforts suffisants ont été faits pour atteindre les objectifs de sécurité. Ainsi, la sécurité du système est souvent justifiée par l'évaluation des arguments de sécurité. L'évaluation de tels arguments repose généralement sur l’avis d’experts sans s’appuyer sur des outils ou des méthodes dédiés. Ceci pose des questions sur la validité des résultats. Dans cette thèse, une approche quantitative est proposée, basé sur la théorie de Dempster-Shafer (théorie D-S) pour évaluer notre confiance dans les arguments de sécurité. Cette approche gère le problème à travers les aspects suivants: 1) Définition formelle de la confiance dans les arguments basée sur la théorie D-S; 2) Développement de règles d'agrégation des paramètres de confiance; 3) Proposition d'un cadre d'évaluation quantitatif des arguments de sécurité. Une application dans le domaine ferroviaire conduit à l'estimation des paramètres du cadre par une enquête auprès d'experts en sécurité. / Safety arguments, also called Safety Cases, are commonly used to present that adequate efforts have been made to achieve the safety goals. Thus, the system safety is often justified through assessing the safety arguments. The assessment of such arguments is usually implemented by experts without any dedicated tool or method. This leads to a questionable validity of the results. In this thesis, a quantitative framework is proposed based on Dempster-Shafer theory (D-S theory) to assess our confidence in Safety Cases. This framework manages the issue in following aspects: 1) Formal definition of confidence in arguments based on D-S theory; 2) Development of confidence aggregation rules; 3) Proposition of a quantitative assessment framework of safety arguments. An application in railway domain realises the parameter estimation of the framework by a survey with safety experts.

With the advent of autonomous systems and deep learning systems, safety pertaining to these systems has become a major concern. The existing failure analysis techniques are not enough to thoroughly analyze the safety in these systems. Moreover, because these systems are created to operate in various conditions, they are susceptible to unknown safety issues. Hence, we need mechanisms which can take into account the complexity of operational design domains, identify safety issues other than failures, and expose unknown safety issues. Moreover, existing safety analysis approaches require a lot of effort and time for analysis and do not consider machine learning (ML) safety. To address these limitations, in this dissertation, we discuss an artificial-intelligence driven model-based methodology that aids in identifying unknown safety issues and analyzing ML safety. Our methodology consists of 4 major tasks: 1) automated model generation, 2) automated analysis of component state transition model specification, 3) undesired states analysis, and 4) causal factor analysis. In our methodology we identify unknown safety issues by finding undesired combinations of components' states and environmental entities' states as well as causes resulting in these undesired combinations. In our methodology, we refer to the behaviors that occur because of undesired combinations as off-nominal behaviors (ONBs). To identify undesired combinations and ONBs that aid in exposing unknown safety issues with less effort and time we proposed various approaches for each of the task and performed corresponding empirical studies. We also discussed machine learning safety analysis from the perspective of machine learning engineers as well as system and software safety engineers. The results of studies conducted as part of our research shows that our proposed methodology helps in identifying unknown safety issues effectively. Our results also show that combinatorial methods are effective in reducing effort and time for analysis of off-nominal behaviors without overlooking any dependencies among components and environmental entities of a system. We also found that safety analysis of machine learning components is different from analysis of conventional software components and detail the aspects we need to consider for ML safety.