This report delves into the early developmental phase of an autonomous vehicle designed for defense applications. Navigating diverse terrains, this unmanned ground vehicle (UGV) poses unique challenges, particularly in the absence of clearly defined directives found in typical traffic scenarios. The analysis employs the Systems-Theoretic Process Analysis (STPA) to identify and anticipate risks inherent in the conceptual stage of product development. Beyond the specific UGV case, the report explores the broader landscape of validating autonomous systems. It discusses prevalent methods, emphasizing adaptability to different contexts and stages of development. By shedding light on the risks and challenges of autonomy in vehicles and examining effective validation strategies, this report aims to contribute valuable insights to the ongoing discourse surrounding autonomous vehicle development.

A logistics centre, or control centre, is a major component for many industries, especially those that participate in activities related to transport vehicles and logistics, for instance, the mining industry. Previous research has shown that the capabilities of humans to process a large amount of information are limited. As the flow of information continues to increase in the future, Situation Awareness (SA) and task performance can decrease. Moreover, currently, an operator is dependent and restricted to their workstation to perform any necessary task, which is also a major hindrance that is being faced. This thesis project used Augmented Reality (AR) to tackle these problems and look at how the user experience of logistics operators working in these control centres could look like and improve in the next few decades. An augmented reality multimodal prototype was developed using a variety of technologies during this thesis project. The prototype was evaluated through both qualitative and quantitative methods. Six subject matter experts (SMEs) were selected as participants and completed a small quantitative survey, after which they were interviewed for more in-depth feedback. Results show a positive approval rating for the prototype, both in terms of usability and learnability. Additionally, a number of further solutions and possible applications emerged from interviews with the participants. Finally, it became apparent that one of the research sub-questions, about the overflow of information, could not be fully addressed by this prototype. However, possible solutions to that problem emerged from the project. / Ett logistikcenter eller kontrollcenter är en viktig komponent för många industrier, särskilt de som deltar i aktiviteter relaterade till transportfordon och logistik, till exempel gruvindustrin. Tidigare forskning har visat att människors förmåga att bearbeta en stor mängd information är begränsad. Eftersom informationsflödet fortsätter att öka i framtiden kan Situation Awareness (SA) och uppgiftsprestanda minska. Dessutom är en operatör för närvarande beroende och begränsad till sin arbetsstation för att utföra alla nödvändiga uppgifter, vilket också är ett stort hinder som står inför. Detta avhandlingsprojekt använde Augmented Reality (AR) för att ta itu med dessa problem och titta på hur användarupplevelsen för logistikoperatörer som arbetar i dessa kontrollcentra kan se ut och förbättras under de närmaste decennierna. En förstärkt verklighet multimodal prototyp utvecklades med hjälp av en mängd olika tekniker under detta avhandlingsprojekt. Prototypen utvärderades genom både kvalitativa och kvantitativa metoder. Sex ämnesexperter (SMF) valdes ut som deltagare och genomförde en liten kvantitativ undersökning, varefter de intervjuades för mer ingående feedback. Resultaten visar ett positivt godkännandebetyg för prototypen, både när det gäller användbarhet och lärbarhet. Dessutom framkom ett antal ytterligare lösningar och möjliga tillämpningar från intervjuer med deltagarna. Slutligen blev det uppenbart att en av forskningsundersökningarna, om överflöd av information, inte helt kunde behandlas av denna prototyp. Men möjliga lösningar på det problemet framkom ur projektet.

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[pt] Veículos autônomos são um problema recente, com aplicação em carros e motocicletas ainda nos estágios iniciais. Além das dificuldades inerentes de fazer um veículo mover-se independentemente, a motocicleta autônoma deve permanecer estável em qualquer velocidade e trajetória. O objetivo principal deste trabalho é desenvolver uma motocicleta elétrica autônoma com sistema de instrumentação de baixo custo. Para tanto, foi analisado um modelo dinâmico de motocicleta, capaz de reproduzir o comportamento real e permitindo a implementação de estratégias de controle linear em tempo real. O controlador tem dois objetivos diferentes: manter a motocicleta estável e seguir uma trajetória desejada, de forma autônoma. Experimentos foram realizados com a motocicleta de escala reduzida com o objetivo de caracterizar seus elementos; as estratégias de controle propostas foram simuladas com o modelo dinâmico ajustado. Por fim, os algoritmos de controle são aplicados ao sistema real através de uma plataforma atuada capaz de reproduzir a dinâmica de veículos de duas rodas. O presente trabalho é uma ferramenta para o ensino de engenharia, envolvendo estudantes de diferentes níveis em torno de um problema complexo. O sistema permite uma aprendizagem contínua com dificuldade crescente, envolvendo temas como dinâmica de multicorpos; análise de resultados através de simulações de software; eletrônica e filtros na instrumentação embutida e técnicas de controle para manter o sistema estável em todos os caminhos desejados, culminando na aplicação experimental dos conceitos citados. / [en] Autonomous vehicles are an interesting and recent problem, with its application in cars and motorcycles still in its early stages. In addition to the inherent difficulties in making a vehicle move independently, the autonomous motorcycle has to be able to remain stable at any speed and trajectory. The vehicle s stability can be achieved by different solutions and control techniques. The main objective of this work is to develop an autonomous electric motorcycle with low cost sensing system. For this, a dynamic model of two-wheeled vehicles is analyzed, capable of describing the dynamic behavior while being simple enough to allow the implementation of real-time linear control strategies. The controller has two different objectives: to maintain the motorcycle stable and to follow a desired trajectory, in an autonomous way. Experiments were carried out with the small scale motorcycle aiming to characterize its elements for the theoretical model; then the proposed control strategies were simulated with the adjusted dynamic model. Finally, the control algorithms are applied to the real system through an actuated platform capable of reproducing the dynamic behavior of single-track vehicles. At last, the present work is a tool for teaching engineering, involving multilevel students around a complex, but familiar, problem. The system allows for continuous learning with increasing difficulty, involving multibody dynamics, experimental results analysis via software simulations, electronics and filters present in the embedded instrumentation and many control techniques to keep the system stable in every desired path, culminating in the experimental application of cited concepts.

For unmanned vehicles, autonomy means that the vehicle’s route can be planned and executed according to some pre-defined rules in the absence of human intervention. Autonomous vehicles (AVs) have become a common type of vehicle for various kinds of transport, for example autonomous forklifts within a warehouse environment. Volvo Autonomous Solution (VAS) works with autonomous vehicles in different areas. To better understand how different methods can be used for planning of autonomous vehicles, VAS initiated this project. To increase the efficiency of AVs, several problems can be examined. One such problem is the allocation problem, also called Multi-Robot Task Allocation, which aims to find out which vehicle should execute which task to achieve a global goal cooperatively. The AVs used by VAS handle Planning Missions (PMs). A PM is, for example, to move goods from a loading point to an unloading point. So, the problem examined in this study is how to assign PMs to vehicles in the most efficient way. The thesis also includes a collection of publications on the area. The problem is solved by using three methods: Mixed Integer Linear Programming (MILP), a Genetic Algorithm that was originally proposed for task assignment in a warehouse environment (GA – Warehouse), and a Genetic Algorithm that was initially proposed for train scheduling (GA – Train). With the MILP method, the problem has been formulated mathematically and the method guarantees an optimal solution. However, the major drawback of this approach is the large computational time required to retrieve a solution. The GA – Warehouse method has a quite simple allocation process but a more complicated path planning part and is, in its entirety, not as flexible as the other methods. The GA – Train method has a lower computational time and can consider many different aspects. All three methods generate similar solutions for the limited set of simple scenarios in this study, but an optimal solution can only be guaranteed by the MILP method. Regardless of which method is used, there is always a trade-off: a guarantee of the optimal solution at the expense of high computational time or a result where no optimal solution can be guaranteed but can be generated quickly. Which method to use depends on the context, what resources are available, and what requirements are placed on the solution. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

<p>  </p> <p>Most new vehicles manufactured in the last two years are connected vehicles (CV) that transmit back to the original equipment manufacturer at near real-time fidelity. These CVs generate billions of data points on an hourly basis, which can provide valuable data to agencies to improve the overall mobility experience for users. However, with this growing scale of CV big data, stakeholders need efficient and scalable methodologies that allow agencies to draw actionable insights from this large-scale data for daily operational use. This dissertation presents a suite of applications, illustrated through case studies, that use CV data for assessing and managing mobility and safety on surface transportation systems.</p> <p>A systematic review of construction zone CV data and crashes on Indiana’s interstates for the calendar year 2019, found a strong correlation between crashes and hard-braking event data reported by CVs. Trajectory-level CV data analyzed for a construction zone on interstate 70 provided valuable insights into travel time and traffic signal performance impacts on the surrounding road network. An 11-state analysis of electric and hybrid vehicle usage in proximity to public charging stations highlighted regions under and overserved by charging infrastructure, providing quantitative support for infrastructure investment allocations informed by real-world usage trends. CV data were further leveraged to document route choice behavior during active freeway incidents providing stakeholders with a historical record of observed routing patterns to inform future alternate route planning strategies. CV trajectory data analysis facilitated the identification of trip chaining activities resulting in improved outlier curation and realistic estimation of travel time metrics.</p> <p>The overall contribution of this thesis is developing analytical big data procedures to process billions of CV data records to inform engineering and public policy investments in infrastructure capacity, highway safety improvements, and new EV infrastructure. These scalable and efficient analysis techniques proposed in this dissertation will help agencies at the federal, state and local levels in addition to private sector stakeholders in assessing transportation system performance at-scale and enable informed data-driven decision making.</p>

Predicting the future state occupancies of pedestrians in urban scenarios is a challenging task, especially considering that conventional methods need an explicit model of the system, hence introducing data-driven reachability analysis. Data-driven reachability analysis uses data, inherently produced by an unknown system, to perform future state predictions using sets, generally represented by zonotopes. These predicted sets are generally more conservative than model-based reachable sets. Therefore, is it possible to cluster previously recorded trajectory data based on the expressed behavior and perform the predictions on each cluster to still be able to provide safety guarantees? The theory behind data-driven reachability analysis, which can handle input noise and model uncertainties and still provide safety guarantees, is quite recent. This means that previous implementations for predicting pedestrians are theoretically probabilistic and would not be appropriate to implement in actual systems. Thus, this thesis is not the first of its kind in predicting the future reachable sets for pedestrians using clustered behavioral data, but it is the first work that provides safety guarantees in the process. The method proposed in this thesis first labels the historically recorded trajectories into the behavior also referred to as mode, the pedestrian expressed, which is done by simple conditional statements. This is done offline. However, this implementation is designed to be modular enabling easier improvements to the labelling system. Then, the reachable sets are computed for each behavior separately, which enables a potential motion planner to decide on which modal sets are relevant for specific scenarios. Theoretically, this method provides safety guarantees. The outcomes of this method were more descriptive reachable sets, meaning that the predicted areas intersected areas that it reasonably should, and did not intersect areas it reasonably should not. Also, the volume of the zonotopes for the modal sets was observed to be smaller than the volume of the implemented baseline, indicating fewer over-approximations and less conservative predictions. These results enable more efficient path planning for Connected and Autonomous Vehicles (CAVs), thus reducing fuel consumption and brake wear. / Att predicera framtida tillstånd för fotgängare i urbana situationer är en utmaning, speciellt med tanke på att konventionella metoder behöver uttryckligen en modell av systemet, därav introduceringen av datadriven nåbarhetsanalys. Datadriven nåbarhetsanalys använder data, naturligt producerad av ett okänt system, för att genomföra framtida tillståndspredicering med hjälp av matematiska set, generellt representerade av zonotoper. Dessa predicerade sets är generellt sett mode konservativa än modellbaserade nåbara set. Därmed, är det möjligt att dela upp historiskt inspelade banor baserat på det uttryckta beteendet och genomföra prediceringar på varje kluster och bibehålla säkerhetsgarantier? Teorin bakom datadriven nåbarhetsanalys, som kan hantera brus i indatat och modellosäkerheter och bibehålla säkerhetsgarantier, är väldigt ny. Detta betyder att tidigare implementationer för att predicera fotgängare är, teoretiskt sett, probabilistiska och är inte lämpliga att implementera i riktiga system. Därmed, detta examensarbete är inte det första som predicerar framtida nåbara set för fotgängare genom att använda kluster för beteendedatat, men den är det första arbetet som bibehåller säkerhetsgarantier i processen. Den introducerade metoden i detta examensarbete rubricerar först de tidigare inspelade banorna baserat på beteendet, även kallat läget, som fotgängaren uttrycker, vilket är gjort genom simpla betingade påståenden. Detta görs offline. Dock, denna implementation är designad till att vara modulär vilket underlättar förbättringar till rubriceringssystemet. Fortsättningsvis, beräknas de nåbara seten för varje beteende separat, vilket möjliggör att en potentiell rörelseplanerare kan avgöra vilka beteendeset som är relevanta för specifika scenarion. Teoretiskt sett så ger denna metod säkerhetsgarantier. Resultaten från denna metod var först och främst mer beskrivande nåbara set, vilket betyder att de predicerade områdena korsar områden som de rimligtvis ska korsa, och inte korsar område som de rimligen inte ska korsa. Dessutom, volymen på zonotoperna for beteendeseten observerades att vara mindre än volymen för baslinjeseten, vilket indikerar lägre överskattningar och mindre konservativa prediceringar. Dessa resultat möjliggör mer effektiv rörelseplanering för uppkopplade och autonoma fordon, vilket reducerar bränsleförbrukningen och bromsslitage.

Automated vehicles (AVs) will be integrated into our society at some point in the future, but when is still up for debate. An extensive amount of research is being completed to understand the communication methods between AVs and other road users sharing the environment to prepare for this future. Currently, researchers are working to understand how different forms of external communication on the AVs will impact vulnerable road user (VRU) interaction. However, within the last 10 years, VRU casualty rates have continued to rise for all classifications of VRUs. Unfortunately, there is no suggestion that pedestrian fatality rates will ever decrease without some intervention. This dissertation aims at understanding the impacts of eHMI across real-world, complex scenarios with AVs and how researchers can apply those future findings to improve VRUs' judgments to today. A series of studies evaluated the necessity and impact of eHMI on AV–VRU interaction, assessed how the visual components of eHMI influenced VRU crossing decisions, and how variations in a real-world environment (multiple vehicles and scenario complexity) impact crossing decision behavior. Two studies examined how eHMI will impact future interactions between AVs and VRUs. Specifically, to understand how to advance the design of these future devices to avoid unintended consequences that may result. Results from these studies found that the presence and condition of eHMI did not influence participants' willingness to cross. Participants primarily relied on the speed and distance of the vehicle to make their crossing decision. It was difficult for participants to focus on the eHMI when multiple vehicles competed for their attention. Participants typically prioritized their focus on the vehicle that was nearest and most detrimental to their crossing path. Additionally, the type of scenario caused participants to make more cautious crossing decisions. However, it did not influence their willingness to cross. The last study applied the learnings from the first two studies to a foundational perception study for current legacy vehicles. These results showed a significant increase in judgment accuracies with a display. Through analysis across overall conclusions from the 3 studies, five critical findings were identified when addressing eHMI and 3 design recommendations, which are discussed in the penultimate section of this work. The results of this dissertation indicate that eHMI improved VRUs' accuracy of perception of change in vehicle speed. eHMI did not significantly impact VRUs crossing decisions. However, the complexity of the traffic scenarios affected the level of caution participants exhibited in their crossing behavior. / Doctor of Philosophy / An extensive amount of research is being completed to understand the communication methods between AVs and other road users sharing the environment to prepare for this future. Currently, researchers are working to understand how different forms of external communication on the AVs will impact vulnerable road user (VRU) interaction. However, within the last 10 years, VRU casualty rates have continued to rise for all classifications of VRUs. Unfortunately, there is no suggestion that pedestrian fatality rates will ever decrease without some intervention. This dissertation aims at understanding the impacts of eHMI across real-world, complex scenarios with AVs and how researchers can apply those future findings to improve VRUs' judgments to today. A series of studies evaluated the necessity and impact of eHMI on AV–VRU interaction, assessed how the visual components of eHMI influenced VRU crossing decisions, and how variations in a real-world environment (multiple vehicles and scenario complexity) impact crossing decision behavior.

<p dir="ltr">Highway safety continues to pose a serious challenge to the social sustainability of transportation systems, and initiatives are being pursued at all levels of government to reduce the high fatality count of 42,000. At the same time, it is sought to ensure higher travel efficiency in order to increase economic productivity. The emergence of automated transportation provides great promise to mitigate these ills of the transportation sector that have persisted for so many decades. With regards to safety, such promise is rooted in the capability of autonomous vehicles to self-drive some or all of the time, thus reducing the impact of inherently errant human driving to which 95% of all crashes have been attributed. With regards to mobility, such promise is guided by the capability of the autonomous vehicle to carry out path planning, navigation, and vehicle controls in ways that are far more efficient than the human brain, thereby facilitating mobility and reducing congestion-related issues such as delay, emissions, driver frustration, and so on.</p><p dir="ltr">Unfortunately, the two key outcomes (safety and mobility) are reciprocal in the sense that navigation solutions that enhance safety generally tend to reduce mobility, and vice versa. As such, there is a need to assign values explicit to these performance criteria in order to develop balanced solutions for AV decisions. Most existing machine-learning-based path planning algorithms derive these weights using a learning approach. Unfortunately, the stability of these weights across time, individuals, and trip types, is not guaranteed. It is necessary to develop weights and processes that are trip situation-specific. Secondly, user trust in automation remains a key issue, given the relatively recent emergence of this technology and a few highly-publicized crashes, which has led to reservations among potential users.</p><p dir="ltr">To address these research questions, this thesis identifies various situational contexts of the problem, identifies the alternatives (the viable trajectories by fitting curves between the vehicle maneuver’s initial and final positions), develops the decision criteria (safety, mobility, comfort), carries out weighting of the criteria to reflect their relative significance, and scales the criteria to develop dimensionless equivalents of their raw values. Finally, a process for amalgamating the overall impacts of each driving decision alternative is developed based on the weighted and scaled criteria, to identify the best decision (optimal trajectory path). This multi-criteria decision making (MCDM) problem involves the collection of data through questionnaire surveys.</p><p dir="ltr">The weights obtained early in the MCDM process could be integrated into any one of two types of planning algorithms. First, they could be incorporated into interpolating curve-based planning algorithms, to identify the optimal trajectory based on human preferences. Additionally, they can be integrated into optimization-based planning algorithms to allocate weights to the various functions used.</p><p dir="ltr">Overall, this research aims to align the behavior of autonomous vehicles closely with human-driven vehicles, serving two primary purposes: first, facilitating their seamless coexistence on mixed-traffic roads and second, enhancing public acceptance of autonomous vehicles.</p>