In recent years, advancements in autonomous driving technologies have accelerated the commercialization of highly automated trucks. This shift away from human drivers raises concerns about the loss of critical functions, particularly in post-diagnosis decision-making, which relies on human inputs in the current practice. This paper outlines the current post-diagnosis decision-making process for human-driven trucks, drawing on insights from industry practitioners, and systematically identifies gaps between these practices and the requirements for highly automated trucks. We propose a comprehensive design of an intelligent decision support system (DSS) to address these gaps. The design includes conducting a system impact analysis to identify new stakeholders, proposing a new DSS architecture with review and learning functions, and concretizing various potentially effective decision-making models and information inputs. Using a real-world freight delivery scenario and a risk-based decision-making approach, we present a case study to instantiate the DSS design, including graphical user interface designs and a step-by-step use case scenario. This work aims to adapt post-diagnosis decision-making for automated trucks at both technological and managerial levels, thereby enhancing vehicle reliability and transport efficiency.

Vehicles are becoming increasingly complex and are prone to faults and failures, which threaten the dependability of vehicles in terms of availability, reliability, safety, and security. When vehicles are detected with certain types of faults and get into alarm situations, human drivers play a vital role in deciding what strategies and actions to take. Once driverless vehicles are introduced, human drivers' roles in decision-making will no longer exist, which urges new solutions on both technological and managerial levels. 

This thesis depicts the current human decision-making process by analyzing field study data in the truck industry, which contributes to gaining domain knowledge and identifying research gaps. An integrated vehicle health management scheme is applied to automate this decision-making process by integrating vehicle health state estimation and prediction, resource utilization, and self-adaptive management. To implement this scheme, fault diagnosis and decision-making methods are proposed, and a decision support system is designed. 

Fault diagnosis is a critical functional module for providing reliable vehicle health state information for decision-making. To address the influence of uncertainties in fault diagnosis, we propose an uncertainty analysis framework and a fault diagnosis method using Bayesian inference.Simulation experiments validate that the proposed method could effectively diagnose the root cause of fault symptoms under environmental uncertainty. 

A risk-based automated decision-making method is presented, which imitates the human decision-making process.On this basis, a collaborative decision-making method is proposed by considering traffic congestion, which is a currently neglected public concern.Experiment results show that the proposed methods could effectively reduce the economic risk and the risk of traffic congestion.

In the end, a decision support system is designed to provide decision information to its human users. Besides, reviewing and learning functions are considered for gaining knowledge and achieving full automation in the long run. Additional system stakeholders from the public sector regarding safety, traffic, and the environment are considered. A transparent, interactive, and adaptive graphical user interface of the system is designed to enhance user experience and trust.

This thesis shows the potential of automated decision-making and technical system design in increasing corporate profits, catalyzing public-private partnerships, enabling technological transformation, and achieving a more sustainable transportation system.

Fordon blir allt mer komplexa och är benägna att få fler felkällor, vilket hotar fordonens pålitlighet när det gäller tillgänglighet, tillförlitlighet och säkerhet.När fordon upptäcks med vissa typer av fel och hamnar i larmsituationer spelar mänskliga förare en avgörande roll när det gäller att bestämma vilka åtgärder som ska vidtas.När förarlösa fordon väl har introducerats kommer mänskliga förares roller i beslutsfattandet inte längre vara tillgängligt, vilket kräver nya lösningar på både teknisk nivå och på ledningsnivå.

Denna avhandling skildrar den nuvarande mänskliga beslutsprocessen genom att analysera fältstudiedata i lastbilsindustrin, vilket bidrar till mer kunskap om området och att identifiera forskningsluckor.Ett integrerat system för fordonshälsa används för att automatisera denna beslutsprocess genom att integrera uppskattning och förutsägelse av fordonets hälsotillstånd, resursanvändning och fordonets möjlighet att själva anpassa sig för att hantera fel.För att implementera detta schema föreslås feldiagnostik och beslutsmetoder, och ett beslutsstödssystem utformas.

Feldiagnos är en kritisk funktionsmodul för att tillhandahålla tillförlitlig information om fordonets hälsotillstånd för beslutsfattande.För att hantera osäkerheter i feldiagnostik, föreslår vi ett ramverk för osäkerhetsanalys och en feldiagnosmetod som använder Bayesiansk slutledning.Simuleringsexperiment bekräftar att den föreslagna metoden effektivt kan diagnostisera grundorsaken till felsymptom, även vid osäkerhet om fordonets kontext.

En riskbaserad automatiserad beslutsmetod presenteras, som imiterar den mänskliga beslutsprocessen.På grundval av detta föreslås en samarbetsmetod för beslutsfattande genom att överväga trafikstockningar, som är ett stort allmänt problem.Experimentresultat visar att de föreslagna metoderna effektivt kan minska den ekonomiska risken och risken för trafikstockningar.

Dessutom har ett system för beslutsstöd utformats för att ge information till mänskliga användare.Dessutom övervägs gransknings- och inlärningsfunktioner för att få kunskap och uppnå full automation på längre sikt.Ytterligare aktörer från offentlig sektor avseende säkerhet, trafik och miljö beaktas även.Ett transparent, interaktivt och adaptivt grafiskt användargränssnitt för systemet är utformat för att förbättra användarupplevelsen och förtroendet.

Denna avhandling visar potentialen hos automatiserat beslutsfattande och teknisk systemdesign för att öka företagens vinster, ökad möjligheten till partnerskap mellan offentlig och privata aktörer samt möjliggöra teknisk transformation och för att uppnå ett mer hållbart transportsystem.

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.

New autonomous driving technologies are emerging every day and some of them have been commercially applied in the real world. While benefiting from these technologies, autonomous trucks are facing new challenges in short-term maintenance planning, which directly influences the truck operator’s profit. In this paper, we implement a vehicle health management system by addressing the maintenance planning issues of autonomous trucks on a transport mission. We also present a maintenance planning model using a risk-based decision-making method, which identifies the maintenance decision with minimal economic risk of the truck company. Both availability losses and maintenance costs are considered when evaluating the economic risk. We demonstrate the proposed model by numerical experiments illustrating real-world scenarios. In the experiments, compared to three baseline methods, the expected economic risk of the proposed method is reduced by up to 47%. We also conduct sensitivity analyses of different model parameters. The analyses show that the economic risk significantly decreases when the estimation accuracy of remaining useful life, the maximal allowed time of delivery delay before order cancellation, or the number of workshops increases. The experiment results contribute to identifying future research and development attentions of autonomous trucks from an economic perspective.

This is the data extraction form for the systematic literature review work for finding critical scenarios for automated driving systems. The extracted data from the primary studies is structured in the following tables. Primary studies in Tables 1 to 5 correspond to the five clusters defined in Section 6 of the main paper. Please note that some primary studies in these tables are classified as out of the scope of the literature study. These studies are marked in the Purpose column. Primary studies in Tables 6 and 7 are eventually considered as out of the scope. The tables are designed aligned with the taxonomy proposed in Section 4 of the main paper. 

Cyber-physical systems (CPS) are paving new ground with increasing levels of automation and usage in applications with complex environments, posing greater challenges in terms of safety and reliability. The increasing complexity of CPS environments, tasks, and systems leads to more uncertainties. Unless properly managed, these uncertainties may lead to false detection of real fault condition of a system, which in turn may affect decision making and potentially cause fatal consequences. In order to implement safety-critical missions, such as autonomous driving, it is essential to develop a reliable monitoring and assessment service dealing with the complexity and uncertainty issues. In this article, we propose a fault detection function based on Bayesian inference, which combines empirical knowledge with information of the specific system. By considering uncertainties as possible causes for false detection, various uncertainties during the detection process are analyzed, and the ways to quantify and propagate them are explored. As a result, probabilistic inference is achieved for distinguishing system faults from uncertainties, which contributes to more reliable detection results regarding system faults under dynamically changing environments. A case study on an microelectro mechanical system (MEMS) accelerometer is conducted and the result shows that the fault detection function effectively distinguishes system faults and uncertainties arising from the environment.

Cyber-Physical Systems (CPS) are a result of highly cross-disciplinary processes and are evolving to perform increasingly challenging tasks in dynamically changing environments. This leads to an increasing CPS complexity and therefore the management of uncertainty to ensure the trustworthiness of these systems is needed. Our paper focuses on uncertainty management (UM) both in general and more specifically in the context of CPS situation awareness (SA). The motivation behind this is the important role of SA and its many inherent uncertainties. To this end, firstly, a literature review is conducted to acquire the state of the art of UM. Later, we present findings and observations from the literature review, with two main challenges identified-inconsistent understanding and terminology among a multitude of uncertainty perspectives, and a lack of collaboration among different communities. On this basis, lastly, two case studies are conducted to exemplify the challenges and provide brief ideas on how to deal with them. The whole investigation in the paper suggests an urgent strengthening of common understanding through enhanced collaboration and regulations.

Constructing and evaluating a comprehensive tool-chain with commercial off-the-shelf and proprietary tools for the deployment of model-based systems engineering (MBSE) is a challenging and complex task. Specifically, the lack of early assessment during tool-chain development has led to increased research and development costs when unexpected features are developed or poor decisions are made. In this paper, a domain-specific modeling (DSM) approach is proposed to support decision-makings during tool-chain design and to facilitate quantitative assessment of tool-chain features at early-phases. Using this approach, different views of tool-chains are first formalized under a DSM framework. Then the DSM models are transformed to Bayesian network models for supporting the quantitative assessment of related tools in order to analyze the whole tool-chains’ features. In the case study, the approach is verified by comparing two MBSE tool-chains for an auto-braking system design. The results indicate that the DSM approach enhances the understanding of tool-chain concepts, promotes the efficiency of MBSE tool-chain development, and verifies the tool-chain in early development phases using a quantitative approach.