For railways, risk analysis is carried out to identify hazardous situations and their consequences. Until recently, classical methods such as Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) were applied in modelling the linear and logically deterministic aspects of railway risks, safety and reliability. However, it has been proven that modern railway systems are rather complex, involving multi-dependencies between system variables and uncertainties about these dependencies. For train derailment accidents, for instance, high train speed is a common cause of failure; slip and failure of brake applications are disjoint events; failure dependency exists between the train protection and warning system and driver errors; driver errors are time dependent and there is functional uncertainty in derailment conditions. Failing to incorporate these aspects of a complex system leads to wrong estimations of the risks and safety, and, consequently, to wrong management decisions. Furthermore, a complex railway system integrates various technologies and is operated in an environment where the behaviour and failure modes of the system are difficult to model using probabilistic techniques. Modelling and quantification of the railway risk and safety problems that involve dependencies and uncertainties such as mentioned above are complex tasks. Importance measures are useful in the ranking of components, which are significant with respect to the risk, safety and reliability of a railway system. The computation of importance measures using FTA has limitation for complex railways. ALARP (As Low as Reasonably Possible) risk acceptance criteria are widely accepted as ’\'best practice’’ in the railways. According to the ALARP approach, a tolerable region exists between the regions of intolerable and negligible risks. In the tolerable region, risk is undertaken only if a benefit is desired. In this case, one needs to have additional criteria to identify the socio-economic benefits of adopting a safety measure for railway facilities. The Life Quality Index (LQI) is a rational way of establishing a relation between the financial resources utilized to improve the safety of an engineering system and the potential fatalities that can be avoided by safety improvement. This thesis shows the application of the LQI approach to quantifying the social benefits of a number of safety management plans for a railway facility. We apply Bayesian Networks and influence diagrams, which are extensions of Bayesian Networks, to model and assess the life safety risks associated with railways. Bayesian Networks are directed acyclic probabilistic graphical models that handle the joint distribution of random variables in a compact and flexible way. In influence diagrams, problems of probabilistic inference and decision making – based on utility functions – can be combined and optimized, especially, for systems with many dependencies and uncertainties. The optimal decision, which maximizes the total benefits to society, is obtained. In this thesis, the application of Bayesian Networks to the railway industry is investigated for the purpose of improving modelling and the analysis of risk, safety and reliability in railways. One example application and two real world applications are presented to show the usefulness and suitability of the Bayesian Networks for the quantitative risk assessment and risk-based decision support in reference to railways.

Alcoholic beverages have been classified as carcinogenic to humans. As alcoholic beverages are multicomponent mixtures containing several carcinogenic compounds, a quantitative approach is necessary to compare the risks. Fifteen known and suspected human carcinogens (acetaldehyde, acrylamide, aflatoxins, arsenic, benzene, cadmium, ethanol, ethyl carbamate, formaldehyde, furan, lead, 4-methylimidazole, N-nitrosodimethylamine, ochratoxin A and safrole) occurring in alcoholic beverages were identified based on monograph reviews by the International Agency for Research on Cancer. The margin of exposure (MOE) approach was used for comparative risk assessment. MOE compares a toxicological threshold with the exposure. MOEs above 10,000 are judged as low priority for risk management action. MOEs were calculated for different drinking scenarios (low risk and heavy drinking) and different levels of contamination for four beverage groups (beer, wine, spirits and unrecorded alcohol). The lowest MOEs were found for ethanol (3.1 for low risk and 0.8 for heavy drinking). Inorganic lead and arsenic have average MOEs between 10 and 300, followed by acetaldehyde, cadmium and ethyl carbamate between 1,000 and 10,000. All other compounds had average MOEs above 10,000 independent of beverage type. Ethanol was identified as the most important carcinogen in alcoholic beverages, with clear dose response. Some other compounds (lead, arsenic, ethyl carbamate, acetaldehyde) may pose risks below thresholds normally tolerated for food contaminants, but from a cost-effectiveness point of view, the focus should be on reducing alcohol consumption in general rather than on mitigative measures for some contaminants that contribute only to a limited extent (if at all) to the total health risk.

Selenium (Se) is an essential micronutrient and is also used in various industrial processes. However, Se also exhibits a low toxicity threshold and therefore presents a significant risk to human kind when released into the environment. The gap between Se deficiency (< 40 µg•day−1) and acute Se poisoning (> 400 µg•day−1) for humans is rather narrow. In addition, detrimental effects to the health of humans and other biota can arise from radioactive Se isotopes. Namely, 79Se is of concern, as it is one of the fission products originating from nuclear power production. The toxicity of selenium not only depends on its concentration but also on its speciation. This of course applies to both stable and radioactive isotopes. Microorganisms play a key role in determining and altering the speciation of Se in the selenium geochemical cycle. The naturally released selenium oxyanions (selenite (SeIVO32−) and selenate (SeVIO42−)) can be microbially reduced to differently shaped biogenic elemental selenium (BioSe, Se(0)) nanomaterials - BioSe-Nanospheres and BioSe-Nanorods. Even after more than 30 years of elaborated research on selenium, the impact of the microbial biota on the shape change of these BioSe-Nanomaterials lacks a fundamental understanding. Furthermore, due to the various species of microorganisms having different metabolisms, a detailed investigation of representative organism is required to predict the fate of selenium in the environment and engineered systems. Thus, the motivation behind this Ph.D. work was to study the effect of selected microorganisms (based on their high resilience, application in wastewater treatment processes, and capability to reduce selenium oxyanions) on the properties and fate of the produced biogenic elemental selenium nanomaterials. Namely, this meant deciphering the role of selenium oxyanion reduction mechanism on the localisation (intracellular or extracellular) of the microbially produced biogenic elemental selenium nanoparticles. This understanding is important as the localisation defines the release of the selenium nanoparticles in the environment and hence its potential pathway into the food chain. Further, the role of the microorganisms (pure culture and mixed culture) on the composition and stability of the corona (organic layer) on the BioSe-Nanomaterials was studied as properties of the corona can affect the stability and hence the localization of the nanomaterials. Moreover, the effect of the microbial environment on the shape establishment and stability, as well as on the fate of the produced biogenic elemental selenium nanomaterials was also investigated. Eventually, the obtained results narrow the identified knowledge gap and improve the understanding of the fate of selenium in the environment. In the first part of this Ph.D. thesis, the bacterial strain Bacillus safensis JG-B5T was chosen to study the influence of microbes on the fate of Se in the environment due to its occurrence in uranium mining sites where selenium is also found. First, this bacterium has been analysed by genome sequencing and its genomic data were deposited at the NCBI database. With the obtained results, the bacterial strain was classified in the corresponding phylogenetic tree. Furthermore, this Ph.D. work revealed that B. safensis JG-B5T is an obligate aerobic microorganism with the ability to reduce SeO32− to elemental selenium (Se(0)) in the form of red BioSe-Nanospheres. A reduction of SeO42− has not been observed. Two-chamber reactor experiments revealed that direct contact between SeO32− and the bacterial cells was necessary to start the reduction. In addition, microscopic investigations identified changes in the bacterial cell morphologies induced by toxic stress effects of SeO32−. Only extracellular production of BioSe-Nanospheres was observed using STEM equipped with a HAADF detector. The produced BioSe-Nanospheres were characterized by Raman spectroscopy as being amorphous Se. Furthermore, a stabilizing corona containing proteins and EPS, which caps the BioSe-Nanospheres, has been identified by FT-IR spectroscopy. The detailed composition of this corona has been further studied using proteomics analysis. The combination of two-chamber reactor experiments, genome analysis and the identified corona proteins indicated that the selenite reduction process of B. safensis JG-B5T was primarily mediated through membrane-associated proteins, like succinate dehydrogenase. Thus, a detailed molecular mechanism of the microbial reduction of SeO32− to BioSe-Nanospheres by the bacterial strain B. safensis JG-B5T has been proposed within this work. Besides these investigations on the formation of BioSe-Nanospheres, ζ-potential measurements have shown a low colloidal stability of the produced BioSe-Nanospheres. Thus, B. safensis JG-B5T is an attractive candidate in selenite wastewater treatment as it provides easy ways of recovering Se while maintaining low Se discharge. These investigations motivated us to study the general role of the microbial origin and microbial environment of the discharged nanomaterials in their shape change from BioSe-Nanospheres to BioSe-Nanorods. This constitutes the second part of this Ph.D. thesis. Thus, two different known microbial BioSe-Nanospheres producers by means of selenite reduction were used, namely the bacterial strain Escherichia coli K-12 and the microbial mix culture of anaerobic granular sludge. It was shown with Raman spectroscopy and SEM imaging that the BioSe-Nanospheres produced by E. coli K-12 remain amorphous and spherical when exposed to thermophilic conditions (up to one year), whereas those obtained by anaerobic granular sludge transform to trigonal BioSe-Nanorods. ζ-potential measurements identified a decrease of the colloidal stability of the transformed BioSe-Nanorods of anaerobic granular sludge compared to the still spherical BioSe-Nanospheres of E. coli K-12. As the shape of these BioSe-Nanospheres is stabilized by their corona, detailed investigations were performed to derive key factors affecting its shape change. CheSeNMs capped with different amount of BSA were produced and incubated to evaluate the quantitative effect of the amount of proteins in the corona on the shape stability of BioSe-Nanomaterials. This experiment implied that the larger quantity of proteins present in the corona of the BioSe-Nanospheres provide better shape stability. Indeed, the BioSe-Nanospheres produced by E. coli K-12 have 5.5 times more protein than those produced by anaerobic granular sludge. To gain deeper insight into their structural properties, proteomics analysis identified the surface proteins of the BioSe-Nanomaterials. The proteomics analysis also showed that the corona of BioSe-Nanospheres produced by E. coli K-12 consists of 1009 different proteins compared to only 173 on those produced by anaerobic granular sludge. The possible difference in the interaction of the corona proteins and selenium was elucidated using density functional theory calculations. The calculations suggest the possibility of the S-Se bond formation between Se atom and sulphur of the cysteine and methionine residues of the corona proteins. Furthermore, as representative for the microbial environment the bacterial strain B. safensis JG-B5T was used to mimic the role of microorganisms living in the vicinity of the discharged nanoparticles. The bacterial strain was incubated with purified BioSe-Nanospheres produced by E. coli K-12 at mesophilic conditions. Raman spectroscopy and SEM imaging showed that in contrast to the thermophilic incubation, the BioSe-Nanospheres transformed to BioSe-Nanorods in the presence of B. safensis JG-B5T. Proteomics analysis identified that the protein corona of BioSe-Nanospheres produced by E. coli K-12 was degraded by extracellular peptidases secreted upon co-incubation with B. safensis JG-B5T bacteria, which led to their transformation to BioSe-Nanorods. All the above findings show, how microorganisms fundamentally impact the speciation, colloidal stability, and shape of selenium. These, consequently, affect their flow coefficients or partition factors in the environment and therefore their fate. This work consequently demonstrates that the shape of the BioSe-Nanomaterials depends on both, their microbial origin and their microbial surrounding. Especially, the dynamic changes induced by this microbial environment on the shape of already formed BioSe-Nanospheres after their discharge are to be further explored. This increases the complexity in determining the risk assessment of Se and probably other redox active elements, which needs to be re-evaluated and improved by including microbial criteria for better accuracy. Based on the presented investigations, further studies regarding the detailed application and expansion to other bacterial strains will continuously widen the understanding of the behaviour of Se in the environment and engineered systems.

Linked to climate change, drivers such as increased temperatures and decreased water availability affect forest health in complex ways by simultaneously weakening tree vitality and promoting insect pest activity. One major beneficiary of climate-induced changes is the European spruce bark beetle (Ips typographus). To improve the mechanistic understanding of climate change impacts on long-term beetle infestation risks, individual-based simulation models (IBM) such as the bark beetle dispersion model IPS-SPREADS have been proven as effective tools. However, the computational costs of IBMs limit their spatial scale of application. While these tools are best suitable to simulate bark beetle dynamics on the plot level, upscaling the process to larger areas is challenging. The larger spatial scale is, nevertheless, often required to support the selection of adequate management intervention. Here, we introduce a novel two-step approach to address this challenge: (1) we use the IPS-SPREADS model to simulate the bark beetle dispersal at a local scale by dividing the research area into 250 × 250 m grid cells; and (2) we then apply a metamodel framework to upscale the results to the landscape level. The metamodel is based on Markov chains derived from the infestation probabilities of IPS-SPREADS results and extended by considering neighbor interaction and spruce dieback of each focal cell. We validated the metamodel by comparing its predictions with infestations observed in 2017 and 2018 in the Saxon Switzerland national park, Germany, and tested sanitation felling as a measure to prevent potential further outbreaks in the region. Validation showed an improvement in predictions by introducing the model extension of beetle spreading from one cell to another. The metamodel forecasts indicated an increase in the risk of infestation for adjacent forest areas. In case of a beetle mass outbreak, sanitation felling intensities of 80 percent and above seem to mitigate further outbreak progression.

For railways, risk analysis is carried out to identify hazardous situations and their consequences. Until recently, classical methods such as Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) were applied in modelling the linear and logically deterministic aspects of railway risks, safety and reliability. However, it has been proven that modern railway systems are rather complex, involving multi-dependencies between system variables and uncertainties about these dependencies. For train derailment accidents, for instance, high train speed is a common cause of failure; slip and failure of brake applications are disjoint events; failure dependency exists between the train protection and warning system and driver errors; driver errors are time dependent and there is functional uncertainty in derailment conditions. Failing to incorporate these aspects of a complex system leads to wrong estimations of the risks and safety, and, consequently, to wrong management decisions. Furthermore, a complex railway system integrates various technologies and is operated in an environment where the behaviour and failure modes of the system are difficult to model using probabilistic techniques. Modelling and quantification of the railway risk and safety problems that involve dependencies and uncertainties such as mentioned above are complex tasks. Importance measures are useful in the ranking of components, which are significant with respect to the risk, safety and reliability of a railway system. The computation of importance measures using FTA has limitation for complex railways. ALARP (As Low as Reasonably Possible) risk acceptance criteria are widely accepted as ’\'best practice’’ in the railways. According to the ALARP approach, a tolerable region exists between the regions of intolerable and negligible risks. In the tolerable region, risk is undertaken only if a benefit is desired. In this case, one needs to have additional criteria to identify the socio-economic benefits of adopting a safety measure for railway facilities. The Life Quality Index (LQI) is a rational way of establishing a relation between the financial resources utilized to improve the safety of an engineering system and the potential fatalities that can be avoided by safety improvement. This thesis shows the application of the LQI approach to quantifying the social benefits of a number of safety management plans for a railway facility. We apply Bayesian Networks and influence diagrams, which are extensions of Bayesian Networks, to model and assess the life safety risks associated with railways. Bayesian Networks are directed acyclic probabilistic graphical models that handle the joint distribution of random variables in a compact and flexible way. In influence diagrams, problems of probabilistic inference and decision making – based on utility functions – can be combined and optimized, especially, for systems with many dependencies and uncertainties. The optimal decision, which maximizes the total benefits to society, is obtained. In this thesis, the application of Bayesian Networks to the railway industry is investigated for the purpose of improving modelling and the analysis of risk, safety and reliability in railways. One example application and two real world applications are presented to show the usefulness and suitability of the Bayesian Networks for the quantitative risk assessment and risk-based decision support in reference to railways.:ACKNOWLEDGEMENTS IV ABSTRACT VI ZUSAMMENFASSUNG VIII LIST OF FIGURES XIV LIST OF TABLES XVI CHAPTER 1: Introduction 1 1.1 Need to model and quantify the causes and consequences of hazards on railways 1 1.2 State-of-the art techniques in the railway 2 1.3 Goals and scope of work 4 1.4 Existing work 6 1.5 Outline of the thesis 7 CHAPTER 2: Methods for safety and risk analysis 10 2.1 Introduction 10 2.1.1 Simplified risk analysis 12 2.1.2 Standard risk analysis 12 2.1.3 Model-based risk analysis 12 2.2 Risk Matrix 14 2.2.1 Determine the possible consequences 14 2.2.2 Likelihood of occurrence 15 2.2.3 Risk scoring matrix 15 2.3 Failure Modes & Effect Analysis – FMEA 16 2.3.1 Example application of FMEA 17 2.4 Fault Tree Analysis – FTA 19 2.5 Reliability Block Diagram – RBD 22 2.6 Event Tree Analysis – ETA 24 2.7 Safety Risk Model – SRM 25 2.8 Markov Model – MM 27 2.9 Quantification of expected values 31 2.9.1 Bayesian Analysis – BA 35 2.9.2 Hazard Function – HF 39 2.9.3 Monte Carlo (MC) Simulation 42 2.10 Summary 46 CHAPTER 3: Introduction to Bayesian Networks 48 3.1 Terminology in Bayesian Networks 48 3.2 Construction of Bayesian Networks 49 3.3 Conditional independence in Bayesian Networks 51 3.4 Joint probability distribution in Bayesian Networks 52 3.5 Probabilistic Inference in Bayesian Networks 53 3.6 Probabilistic inference by enumeration 54 3.7 Probabilistic inference by variable elimination 55 3.8 Approximate inference for Bayesian Networks 57 3.9 Dynamic Bayesian Networks 58 3.10 Influence diagrams (IDs) 60 CHAPTER 4: Risk acceptance criteria and safety targets 62 4.1 Introduction 62 4.2 ALARP (As Low As Reasonably Possible) criteria 62 4.3 MEM (Minimum Endogenous Mortality) criterion 63 4.4 MGS (Mindestens Gleiche Sicherheit) criteria 64 4.5 Safety Integrity Levels (SILs) 65 4.6 Importance Measures (IMs) 66 4.7 Life Quality Index (LQI) 68 4.8 Summary 72 CHAPTER 5: Application of Bayesian Networks to complex railways: A study on derailment accidents 73 5.1 Introduction 73 5.2 Fault Tree Analysis for train derailment due to SPAD 74 5.2.1 Computation of importance measures using FTA 75 5.3 Event Tree Analysis (ETA) 78 5.4 Mapping Fault Tree and Event Tree based risk model to Bayesian Networks 79 5.4.1 Computation of importance measures using Bayesian Networks 81 5.5 Risk quantification 82 5.6 Advanced aspects of example application 83 5.6.1 Advanced aspect 1: Common cause failures 83 5.6.2 Advanced aspect 2: Disjoint events 84 5.6.3 Advanced aspect 3: Multistate system and components 84 5.6.4 Advanced aspect 4: Failure dependency 85 5.6.5 Advanced aspect 5: Time dependencies 85 5.6.6 Advanced aspect 6: Functional uncertainty and factual knowledge 85 5.6.7 Advanced aspect 7: Uncertainty in expert knowledge 86 5.6.8 Advanced aspect 8: Simplifications and dependencies in Event Tree Analysis 86 5.7 Implementation of the advanced aspects of the train derailment model using Bayesian Networks. 88 5.8 Results and discussions 92 5.9 Summary 93 CHAPTER 6: Bayesian Networks for risk-informed safety requirements for platform screen doors in railways 94 6.1 Introduction 94 6.2 Components of the risk-informed safety requirement process for Platform Screen Door system in a mega city 97 6.2.1 Define objective and methodology 97 6.2.2 Familiarization of system and information gathering 97 6.2.3 Hazard identification and hazard classification 97 6.2.4 Hazard scenario analysis 98 6.2.5 Probability of occurrence and failure data 99 6.2.6 Quantification of the risks 105 6.2.6.1. Tolerable risks 105 6.2.6.2. Risk exposure 105 6.2.6.3. Risk assessment 106 6.3 Summary 107 CHAPTER 7: Influence diagrams based decision support for railway level crossings 108 7.1 Introduction 108 7.2 Level crossing accidents in railways 109 7.3 A case study of railway level crossing 110 7.4 Characteristics of the railway level crossing under investigation 111 7.5 Life quality index applied to railway level crossing risk problem 115 7.6 Summary 119 CHAPTER 8: Conclusions and outlook 120 8.1 Summary and important contributions 120 8.2 Originality of the work 122 8.3 Outlook 122 BIBLIOGRAPHY 124 APPENDIX 1 131

Trotz Fortschritte in den letzten Dekaden ist das Langzeitüberleben von Nierentransplantaten unzureichend. Die Personalisierung der Behandlung kann dabei zu erheblichen Verbesserungen führen. Vor diesem Hintergrund wurde eine Kohorte von 587 Patienten im ersten Jahr nach der Transplantation untersucht und ein breites Spektrum von Markern zur langfristigen Prognose etabliert. In dieser Dissertation beschreibe ich in vier Manuskripten und zwei Kapiteln meine Arbeit zur personalisierten Transplantationsmedizin. Der klinische Verlauf von Patienten nach Nierentransplantation wurde untersucht. Die wichtigen Komplikationen standen im Vordergrund: Virusreaktivierungen – insbesondere die BK- und Cytomegalieviren – und akute Abstoßung. Folgende Analysen wurden durchgeführt: (i) Systematische Analyse der Assoziationen zwischen Virusreaktivierungen und deren Einfluss auf das Transplantationsergebnis; (ii) Bewertung der Auswirkungen antiviraler Behandlungsstrategien auf die Transplantationsergebnisse; (iii) Entwicklung eines Tools zur Prätransplantations-Risikoeinschätzung der Abstoßung und (iv) Erstellung eines mathematischen Modells für die personalisierte Charakterisierung der Immunantwort gegen das BK-Virus. Zusammengenommen haben die vier Studien das Potenzial, (i) die Patientenversorgung zu verbessern, (ii) die Überwachung von Virusreaktivierungen zu optimieren, (iii) Präventionsstrategien gegen virale Reaktivierungen zu stratifizieren, (iv) die Behandlung der Patienten an das individuelle Risiko akuter Abstoßung anzupassen, und (v) zur Personalisierung der Immuntherapie beizutragen. Die Studien zeigen, wie das große Datenvolumen einer klinischen Studie zur Weiterentwicklung der personalisierten Medizin unter Einsatz effektiver Strategien für Datenmanagement, Analyse und Interpretation genutzt werden kann. Es ist zu erwarten, dass diese Ergebnisse die klinische Praxis beeinflussen und so das langfristige Überleben und die Lebensqualität der Patienten verbessern. / In spite of the developments in the last decades, long-term graft survival rates in kidney transplantation are still poor: Personalization of treatment can thereby lead to a drastic improvement in long-term outcomes. With this goal, a cohort of 587 patients was characterized for a wide range of markers during the first post-transplantation year to assess their long-term prognosis. Here, I describe along four manuscripts and two chapters my work on personalized medicine for renal transplantation. In detail, we have studied the clinical evolution of patients with emphasis on two most relevant complications: viral reactivations – particularly those of BK virus and cytomegalovirus – and acute rejection. We have analysed in depth these phenomena by (i) exhaustively analysing the associations between different viral reactivations and their influence on transplantation outcome, (ii) evaluating the effects of antiviral treatment strategies on viral reactivation and other transplantation outcomes with emphasis on sex-associated differences, (iii) developing a tool for the pre-transplantation risk assessment of acute cellular rejection, and (iv) creating a mathematical model for the personalized characterization of the immune response against the BK virus under immunosuppression. Taken together, these studies have the potential of improving patient care, optimizing monitoring of viral reactivations, stratifying antiviral prevention strategies, tailoring immunosuppression and monitoring to the individual risk of acute rejection, and contributing to personalization of immunotherapy. They demonstrate how the large volume of data obtained within a clinical study can be employed to further the development of personalized medicine, employing effective data management, analysis and interpretation strategies. We expect these results to eventually inform clinical practice, thereby improving long-term survival and quality of life after kidney transplantation.

New automated vehicles have the chance of high improvements to road safety. Nevertheless, from today's perspective, accidents will always be a part of future mobility. Following the “Vision Zero”, this thesis proposes the quantification of the driving situation's criticality as the basis to intervene by newly integrated safety systems. In the example application of trajectory planning, a continuous, real-time, risk-based criticality measure is used to consider uncertainties by collision probabilities as well as technical accident severities. As result, a smooth transition between preventative driving, collision avoidance, and collision mitigation including impact point localization is enabled and shown in fleet data analyses, simulations, and real test drives. The feasibility in automated driving is shown with currently available test equipment on the testing ground. Systematic analyses show an improvement of 20-30 % technical accident severity with respect to the underlying scenarios. That means up to one-third less injury probability for the vehicle occupants. In conclusion, predicting the risk preventively has a high chance to increase the road safety and thus to take the “Vision Zero” one step further.:Abstract Acknowledgements Contents Nomenclature 1.1 Background 1.2 Problem statement and research question 1.3 Contribution 2 Fundamentals and relatedWork 2.1 Integral safety 2.1.1 Integral applications 2.1.2 Accident Severity 2.1.2.1 Severity measures 2.1.2.2 Severity data bases 2.1.2.3 Severity estimation 2.1.3 Risk assessment in the driving process 2.1.3.1 Uncertainty consideration 2.1.3.2 Risk as a measure 2.1.3.3 Criticality measures in automated driving functions 2.2 Operational motion planning 2.2.1 Performance of a driving function 2.2.1.1 Terms related to scenarios 2.2.1.2 Evaluation and approval of an automated driving function 2.2.2 Driving function architecture 2.2.2.1 Architecture 2.2.2.2 Planner 2.2.2.3 Reference planner 2.2.3 Ethical issues 3 Risk assessment 3.1 Environment model 3.2 Risk as expected value 3.3 Collision probability and most probable collision configuration 4 Accident severity prediction 4.1 Mathematical preliminaries 4.1.1 Methodical approach 4.1.2 Output definition for pedestrian collisions 4.1.3 Output definition for vehicle collisions 4.2 Prediction models 4.2.1 Eccentric impact model 4.2.2 Centric impact model 4.2.3 Multi-body system 4.2.4 Feedforward neural network 4.2.5 Random forest regression 4.3 Parameterisation 4.3.1 Reference database 4.3.2 Training strategy 4.3.3 Model evaluation 5 Risk based motion planning 5.1 Ego vehicle dynamic 5.2 Reward function 5.3 Tuning of the driving function 5.3.1 Tuning strategy 5.3.2 Tuning scenarios 5.3.3 Tuning results 6 Evaluation of the risk based driving function 6.1 Evaluation strategy 6.2 Evaluation scenarios 6.3 Test setup and simulation environment 6.4 Subsequent risk assessment of fleet data 6.4.1 GIDAS accident database 6.4.2 Fleet data Hamburg 6.5 Uncertainty-adaptive driving 6.6 Mitigation application 6.6.1 Real test drives on proving ground 6.6.2 Driving performance in simulation 7 Conclusion and Prospects References List of Tables List of Figures A Extension to the tuning process