| 1. |
- Zhang, Chi, et al.
(författare)
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Crowd-funding Intention and Willingness-to-pay for a Sustainable Milk Product with Integrated Photovoltaic Water Pumping System in China
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In comparison with current financing mechanisms for renewable energy systems, crowd-funding financing mechanism offers a new potential source of financing with recent use of social media. Crowd-funding financing mechanism can also increases the social supports for renewable energy systems as users and investors turn to be more actively engaged in energy systems. As a new potential source of financing, crowd-funding mechanism has different forms, including donation, lending, equity and product reward approaches. In this paper, discrete choice model was used to explore whether crowd-funding financing with a novel sociotechnical product reward practice, has the attractions for potential customers to pay for a more sustainable milk product with distributed photovoltaic (PV) system. We empirically investigated the product reward crowd funding with the specific integrated photovoltaic water pumping (PVWP) system in dairy milk production in China. 48 in-depth interviews were adopted for qualitative analysis of determinants of customer milk purchase decision. The ordered probit regression was employed with 357 online surveys to systematically estimate the purchase intention and willingness to pay value for the online-crowd-funding sustainable milk. Customer behaviours, environmental consciousness, and the individual socio-demographic factors were tested as potential explanatory variables. In the survey and depth interview samples, we found interviewees as potential customers showed strong purchase intentions to the crowd funding dairy milk for noticing milk quality and nutritious improvement, emission reduction and environmental benefits by the integrated PVWP system. In our findings of the regression results, the potential customers with higher income-level, at the age with young children or planning to have children were found with higher willingness to pay than other customers for crowd funding the sustainable dairy milk. The familiarity and popularity with online shopping and pre-sale purchase in China made customers more open and active towards pre-pay and crowd-funding mechanism.
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| 2. |
- Berger, Christian, 1980, et al.
(författare)
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The use of AI in AV human-factors research and human-factors requirements in AI-based AV design : Deliverable 2.4 in the EC ITN project SHAPE-IT
- 2023
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The design of automated vehicles (AVs) today is being enabled by the rise of new technologies, actually in particular recent advances in Artificial Intelligence (AI). Navigating the challenges and potential of this technology is crucial for the organizations that develop AVs, as well as for societies that rely on smart transportation. In this report, we consider two perspectives on these technologies in AV research and design, with a particular focus on human factors (HF): (A) Human-Factors Requirements in AV Development, and (B) The Use of AI in Research about Vehicle-Human-Interaction. We describe each part separately; they are different enough to stand on their own, while both descriptions together make up this report. We start with the first perspective – investigating how AI can facilitate HF research and practical use of AI to predict human behaviour for use by HF designers. To support HF researchers and automation designers with tools for classifying and predicting interaction behaviours between AVs/vehicles and pedestrians in urban environments, we developed AI-based models (eg., Zhang et al., 2023) to predict the outcomes of pedestrian-vehicle interactions at unsignalised crossings. The models include random forest models, support vector machine models, and neural network models. The input consists of multiple features such as time to arrival (TTA), pedestrian waiting time, presence of a zebra crossing, and properties and personality traits of both pedestrians and drivers. The output consists of interaction outcomes such as crossing behaviour, crossing duration, and crossing initiation time. The predicted outcomes can contribute to a better understanding of the interactions. In addition, we analysed the interaction factors in order to support HF researchers and automation designers in their efforts to design safer interaction interface. We reviewed a large selection of papers that used AI to predict pedestrian behaviour and interactions (Zhang and Berger, 2023). We proposed a framework of AI-based tools for predicting pedestrian behaviours and summarized some guidelines for using AI—especially deep learning methods for pedestrian behaviour and interaction prediction. Furthermore, our own body of work (Zhang et al., 2021, Zhang and Berger, 2022a, Zhang and Berger, 2022b, Zhang et al., 2023) provides detailed steps for developing an example of an AI model. A key contribution of our research is metrics that allow the evaluation and assessment of AI’s success at classifying and predicting pedestrian-vehicle interactions. In our study, we compared AI models with traditional linear models (Zhang et al., 2023). Further, we compared the performance of AI models and traditional methods with fewer input factors; traditional methods perform well when there are fewer, while AI-based methods perform better when dealing with more input factors. This finding provides information for optimal model selection in different scenarios. To summarize, our findings suggest that AI can help us understand the intentions of human actors and predict their next steps when they interact with AVs. The second perspective investigates how HF research can facilitate AV development activities. We had anticipated that the reliance of AV on AI technology might play a major role in how developers need to think about HF (hence, this aspect is also reflected in the title of this report). Our reasoning was that AI-based AV provide a larger surface of interaction between humans and AVs, not only through the traditional human machine interface. However, early in the project we identified that there was a need to address not only the AI-based aspects of HF requirements in AV development, but also to address HF requirements overall in AV development – not the least within agile ways of working. We therefore decided to include AI-based AV development considerations as part of the larger scope of studying HF requirements in the context of AV development, with focus on agile processes. The agile angle was chosen as AV development increasingly incorporates agile and continuous development approaches. We find that it is conceptually unclear how to systematically incorporate HF in such a fast-paced environment. Further, the automotive industry used as our subject of study is lacking guidelines (as well as best practices) for incorporating HF into these ways of working. We propose the development and application of a HF requirements strategy to manage key implications, for which our research suggests useful templates and guidelines.
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| 3. |
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| 4. |
- Dozza, Marco, 1978, et al.
(författare)
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Data and data collection methodologies for the development of computational models of AV/VRU interaction and their integration into virtual simulation testing of AV : Deliverable 2.3 in the EC ITN project SHAPE-IT
- 2023
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Several computational models explaining interactions between AVs and the VRUs pedestrians and cyclists have been developed in SHAPE-IT. For instance, there are now models predicting whether a pedestrian or cyclist will cross or yield at an intersection. Further, interaction models were developed and/or verified using different types of data collected in experiments or 'in the wild'. These data were combined and fed to different algorithms that leveraged machine learning to describe road-user behaviour. This deliverable address both pedestrian and cyclist interactions with AVs, utilising both naturalistic data and data collected in controlled environments. The former comprised site-based and in-vehicle data collections. The latter included data from several virtual environments (e.g., driving simulators, riding simulators, and pedestrian simulation environments). The main conclusion of this deliverable is that the potential for computational models of AV/VRU interaction to promote AV safety while reducing the cost and time of AV development is high. However, more data is needed before human behaviour (especially in critical scenarios) is captured precisely and comprehensively enough that their integration into virtual simulations delivers explainable, accurate, and reliable results. This deliverable is rather a stepping stone to be used to define intermediate goals for the eventual development of computational models of AV/VRU interaction and their integration into virtual simulations for safety benefit assessment. Within SHAPE-IT, ESR3, ESR13, and ESR14 developed everyday-driving models that may be used directly in traffic simulations, while the focus of ESR15 has been on methods related to and applications of counterfactual simulations.
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| 5. |
- Figalova, Nikol, et al.
(författare)
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Methodological Framework for Modelling and Empirical Approaches (Deliverable D1.1 in the H2020 MSCA ITN project SHAPE-IT)
- 2021
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The progress in technology development over the past decades, both with respect to software and hardware, offers the vision of automated vehicles as means of achieving zero fatalities in traffic. However, the promises of this new technology – an increase in road safety, traffic efficiency, and user comfort – can only be realized if this technology is smoothly introduced into the existing traffic system with all its complexities, constraints, and requirements. SHAPE- IT will contribute to this major undertaking by addressing research questions relevant for the development and introduction of automated vehicles in urban traffic scenarios. Previous research has pointed out several research areas that need more attention for a successful implementation and deployment of human-centred vehicle automation in urban environments. In SHAPE-IT, for example, a better understanding of human behaviour and the underlying psychological mechanisms will lead to improved models of human behaviour that can help to predict the effects of automated systems on human behaviour already during system development. Such models can also be integrated into the algorithms of automated vehicles, enabling them to better understand the human interaction partners’ behaviours. Further, the development of vehicle automation is much about technology (software and hardware), but the users will be humans and they will interact with humans both inside and outside of the vehicle. To be successful in the development of automated vehicles functionalities, research must be performed on a variety of aspects. Actually, a highly interdisciplinary team of researchers, bringing together expertise and background from various scientific fields related to traffic safety, human factors, human-machine interaction design and evaluation, automation, computational modelling, and artificial intelligence, is likely needed to consider the human-technology aspects of vehicle automation. Accordingly, SHAPE-IT has recruited fifteen PhD candidates (Early Stage Researchers – ESRs), that work together to facilitate this integration of automated vehicles into complex urban traffic by performing research to support the development of transparent, cooperative, accepted, trustworthy, and safe automated vehicles. With their (and their supervisors’) different scientific background, the candidates bring different theoretical concepts and methodological approaches to the project. This interdisciplinarity of the project team offers the unique possibility for each PhD candidate to address research questions from a broad perspective – including theories and methodological approaches of other interrelated disciplines. This is the main reason why SHAPE-IT has been funded by the European Commission’s Marie Skłodowska-Curie Innovative Training Network (ITN) program that is aimed to train early state researchers in multidisciplinary aspects of research including transferable skills. With the unique scope of SHAPE-IT, including the human-vehicle perspective, considering different road-users (inside and outside of the vehicle), addressing for example trust, transparency, and safety, and including a wide range of methodological approaches, the project members can substantially contribute to the development and deployment of safe and appreciated vehicle automation in the cities of the future. To achieve the goal of interdisciplinary research, it is necessary to provide the individual PhD candidate with a starting point, especially on the different and diverse methodological approaches of the different disciplines. The empirical, user-centred approach for the development and evaluation of innovative automated vehicle concepts is central to SHAPE- IT. This deliverable (D1.1 “Methodological Framework for Modelling and Empirical Approaches”) provides this starting point. That is, this document provides a broad overview of approaches and methodologies used and developed by the SHAPE-IT ESRs during their research. The SHAPE-IT PhD candidates, as well as other researchers and developers outside of SHAPE-IT, can use this document when searching for appropriate methodological approaches, or simply get a brief overview of research methodologies often employed in automated vehicle research. The first chapter of the deliverable shortly describes the major methodological approaches to collect data relevant for investigating road user behaviour. Each subchapter describes one approach, ranging from naturalistic driving studies to controlled experiments in driving simulators, with the goal to provide the unfamiliar reader with a broad overview of the approach, including its scope, the type of data collected, and its limitations. Each subchapter ends with recommendations for further reading – literature that provide much more detail and examples. The second chapter explains four different highly relevant tools for data collection, such as interviews, questionnaires, physiological measures, and as other current tools (the Wizard of Oz paradigm and Augmented and Virtual Reality). As in the first chapter this chapter provides the reader with information about advantages and disadvantages of the different tools and with proposed further readings. The third chapter deals with computational models of human/agent interaction and presents in four subchapters different modelling approaches, ranging from models based on psychological mechanisms, rule-based and artificial intelligence models to simulation models of traffic interaction. The fourth chapter is devoted to Requirements Engineering and the challenge of communicating knowledge (e.g., human factors) to developers of automated vehicles. When forming the SHAPE-IT proposal it was identified that there is a lack of communication of human factors knowledge about the highly technical development of automated vehicles. This is why it is highly important that the SHAPE-IT ESRs get training in requirement engineering. Regardless of the ESRs working in academia or industry after their studies it is important to learn how to communicate and disseminate the findings to engineers. The deliverable ends with the chapter “Method Champions”. Here the expertise and association of the different PhD candidates with the different topics are made explicit to facilitate and encourage networking between PhDs with special expertise and those seeking support, especially with regards to methodological questions.
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| 6. |
- Figalova, Nikol, et al.
(författare)
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Perceived safety and predictability of human/AV interactions : Deliverable 1.2 in the EC ITN project SHAPE-IT
- 2023
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In the fast-evolving landscape of transportation, the integration of Automated Vehicles (AVs) holds immense promise for revolutionizing mobility and enhancing road safety. As we embark on this transformative journey, it is imperative that we investigate and understand the intricacies of the interactions between humans and AVs, because successful integration of AVs into the urban fabric hinges not only on their technical proficiency but also on human factors. Consequently, the fundamental premise of our exploration is that a thorough understanding of the perceived safety and predictability of AVs is of key importance. Supporting the development of AVs that are perceived as safe requires the consideration and integration of different aspects and perspectives on predictability that are highly related to each other. On the one hand, the drivers and passengers inside the vehicle must, at least in principle, be able to understand and predict the behaviour of the AV, because any unexpected behaviour would reduce their acceptance and trust—and consequently their willingness to adopt this technology. To achieve this goal, the AV’s behaviour (and, ideally, its underlying goals and plans) must be made transparent to the humans inside the vehicle. On the other hand, the behaviour— specifically the movement—of an AV depends in part on the movements of the surrounding humans. In highly dynamic, social environments such as urban traffic, the ability of AVs to predict the movement of vulnerable road users (VRUs) is therefore paramount. The AVs’ proactive and anticipatory responses can enhance overall road safety and contribute to their efficient, harmonious integration into shared urban environments. Thus, to some degree, these prediction functions must be transparent to the humans inside the AV, to facilitate predictability of AV behaviour (so that the humans in the AVs know what to expect) and to ensure that they experience an appropriate level of perceived safety. Although this deliverable is in SHAPE-IT work package 1 ("Safe and transparent interactions between AVs and humans inside the AVs"), we have included both in-AV and outside-AV perspectives. This decision was motivated by the interdependence of these two perspectives and the multi-disciplinary emphasis of SHAPE-IT. Essentially, we addressed the multifaceted challenges presented by the predictability of human movement behaviour from both inside and outside the AV. Our empirical results shed light on many nuances of the factors that influence the subjective sense of safety experienced by individuals as they interact with AVs. Our approaches included subjective evaluations, neuroergonomics methodologies, and modelling approaches—as well as revelations about the degree to which AV-VRU interactions can be accurately predicted. In the three chapters of this deliverable, we will explain our findings, which reveal the complex tapestry of human-AV interactions. Results from empirical and modelling research on the perceived safety of AVs and the predictability of human-AV interactions will be presented. By revealing the interwoven threads of perceived safety and predictability, we hope to contribute not only to academic discourse but also to the practical implementation of AV technologies. Our endeavour aligns seamlessly with the European Union's commitment to fostering innovation that prioritizes human-centric design, thereby ensuring that future mobility is not just automated but, more importantly, safe, and predictable for all.
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| 7. |
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| 8. |
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| 9. |
- Markkula, Gustav, et al.
(författare)
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Behavioural models explaining and predicting transparent negotiations between AVs and human road users : Deliverable 2.2 in the EC ITN project SHAPE-IT
- 2023
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- One key enabler for the development of automated vehicles that can coexist and negotiate with human road users in a safe, transparent, and human-acceptable manner is the development of mathematical models of human behaviour in the relevant interactive scenarios. Such models are needed as components both in real-time AV algorithms and in simulation tools for virtual AV testing. However, many important questions about how to model human road user interactions remain unanswered, and five of the Early Stage Researchers (ESRs) in the SHAPE-IT project have targeted such research questions. This report provides an overall introduction to the area of human-AV interaction modelling and summarises the ESRs’ research and findings in this area, including links to the ESRs’ peer-reviewed papers and preprints (providing full details). The SHAPE-IT modelling research by these ESRs has spanned a broad spectrum of modelling approaches and modelling use cases and has generated results such as: More computationally effective and transferable algorithms for real-time prediction of pedestrian movement; novel insights about human driver communication and behaviour during lane changes; a general model of human AV passengers’ subjective perception of traffic risk; a demonstration that models of vehicle-pedestrian interactions based on behavioural game theory outperform conventional game theory models; and novel insights about how cyclists’ head, eye, and pedalling behaviour predict cyclist-vehicle interactions. The research by these five ESRs has increased our understanding of, and capability to predict and simulate, how humans interact in traffic, with direct relevance for development of safe and human-acceptable AVs.
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| 10. |
- Pokorny, Fabian, et al.
(författare)
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Magic trapping of a Rydberg ion with a diminished static polarizability
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Highly excited Rydberg states are usually extremely polarizable and exceedingly sensitive to electric fields. Because of this Rydberg ions confined in electric fields have state-dependent trapping potentials. We engineer a Rydberg state that is insensitive to electric fields by coupling two Rydberg states with static polarizabilities of opposite sign, in this way we achieve state-independent magic trapping. We show that the magically-trapped ion can be coherently excited to the Rydberg state without the need for control of the ion's motion.
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