| 1. |
- de Winter, Joost, et al.
(författare)
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Design strategies and prototype HMI designs for pedestrians, cyclists, and non-automated cars : Deliverable D2.5 in the EC ITN project SHAPE-IT
- 2023
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This work provides a summary of work within the project SHAPE-IT (Supporting the interaction of Humans and Automated vehicles: Preparing for the Environment of Tomorrow) concerning HMI design for pedestrians, cyclists, and to some extent car drivers. We present the lessons learned from doctoral candidates (ESRs) who were involved with HMI design using augmented reality and connectivity. The lessons learned, which are discussed in this work, relate to if and how human-machine-interface (HMI) information should be presented to end users. The underlying philosophy is that through augmented reality (AR) and connectivity, virtual information in the form of warnings, instructions, and affordances can essentially be displayed at any location in the environment, or even be removed from the environment, to, for example, create transparent objects. However, just because something falls within the realm of technical possibilities and is theoretically interesting, does not imply that users will understand the information and can process it efficiently, or whether they would find it worthwhile and acceptable compared to no information or more traditional forms of HMI communication. This deliverable should serve as a useful reference for researchers and HMI designers who are involved in road transport. The report is structured as a core with accompanying already published journal articles as appendices.
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| 2. |
- 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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| 3. |
- Fan, Jieyu, et al.
(författare)
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Analyzing and Optimizing the Emission Impact of Intersection Signal Control in Mixed Traffic
- 2023
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Ingår i: SUSTAINABILITY. - 2071-1050. ; 15:22
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Tidskriftsartikel (refereegranskat)abstract
- Signalized intersections are one of the typical bottlenecks in urban transport systems that have reduced speeds and which have substantial vehicle emissions. This study aims to analyze and optimize the impacts of signal control on the emissions of mixed traffic flow (CO, HC, and NOx) containing both heavy- and light-duty vehicles at urban intersections, leveraging high-resolution field emission data. An OBEAS-3000 (Manufacturer: Xiamen Tongchuang Inspection Technology Co., Ltd., Xiamen, China.) vehicle emission testing device was used to collect microscopic operating characteristics and instantaneous emission data of different vehicle types (light- and heavy-duty vehicles) under different operating conditions. Based on the collected data, the VSP (Vehicle Specific Power) model combined with the VISSIM traffic simulation platform was used to quantitatively analyze the impact of signal control on traffic emissions. Heavy-duty vehicles contribute to most of the emissions regardless of the low proportion in the traffic flows. Afterward, a model is proposed for determining the optimal signal control at an intersection for a specific percentage of heavy-duty vehicles based on the conversion of emission factors of different types of vehicles. Signal control is also optimized based on conventional signal timing, and vehicle emissions are calculated. In the empirical analysis, the changes in CO, HC, and NOx emissions of light- and heavy-duty vehicles before and after conventional signal control optimization are quantified and compared. After the signal control optimization, the CO, HC, and NOx emissions of heavy-duty vehicles were reduced. The CO and HC emissions of light-duty vehicles were reduced, but the NOx emissions of light-duty vehicles remained unchanged. The emissions of vehicles after optimized signal control based on vehicle conversion factors are reduced more significantly than those after conventional optimized signal control. This study provides a scientific basis for developing traffic management measures for energy saving and emission reduction in transport systems with mixed traffic.
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| 4. |
- Fan, Jieyu, et al.
(författare)
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Evaluating the Impact of Signal Control on Emissions at Intersections
- 2022
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Ingår i: Smart Innovation, Systems and Technologies. - Singapore : Springer Nature Singapore. - 2190-3026 .- 2190-3018. ; 304 SIST, s. 104-111
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Konferensbidrag (refereegranskat)abstract
- Transport emission has become an increasingly serious problem, and it is an urgent issue in sustainable transport. In this study, by constructing traffic emission models for different vehicle types and operating conditions, the changes in CO, HC, and NOx emissions of light-duty and heavy-duty vehicles before and after signal control optimization were quantified based on VISSIM simulation. The OBEAS-3000 vehicle emission testing device was used to collect data on the micro-operational characteristics of different vehicles under different operating conditions as well as traffic emission data. Based on the data collected, the VSP (Vehicle Specific Power) model combined with the VISSIM traffic simulation platform was used to calculate the emissions of light and heavy vehicles in the mixed traffic flow before and after intersection signal optimization. It is known from the study that signal control optimization has a greater impact on heavy vehicles than on light vehicles. Emissions of CO, HC, and NOx from heavy vehicles and light vehicles are all reduced, but NOx emissions from light vehicles remain largely unchanged. The research results reveal the emission patterns of light and heavy vehicles in different micro-operating conditions and establish a traffic emission model. It provides a theoretical basis for accurate traffic emission analysis and traffic flow optimization, as well as a scientific basis for the formulation of traffic management measures and emission reduction in large city transport systems.
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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. |
- Jokhio, Sarang, et al.
(författare)
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Analysis of Time-to-Lane-Change-Initiation Using Realistic Driving Data
- 2024
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 25:5, s. 4620-4633
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Tidskriftsartikel (refereegranskat)abstract
- Lane changing is a complex, yet extremely common driving manoeuvre. Studying lane changes can provide insight into how long drivers wait after activating their turn signal before changing lanes -a time that we call time-to-lane-change-initiation (TTLCI). TTLCI can offer valuable insights into driver behaviour prior to changing lanes. However, a better understanding of TTLCI, particularly in real-world settings, is lacking. To address this knowledge gap, we investigated TTLCI using driving data collected on public roads in Gothenburg, Sweden. We used the Kaplan-Meier (K-M) method and the mixed-effect Cox Proportional Hazard (CPH) model (statistical techniques from survival analysis) to comprehensively analyze TTLCI and identify factors that significantly influence it. The results of the K-M method indicate that most lane changes were initiated within two seconds of activating the turn signal. The mixed-effect CPH model showed that the speed of the lane-changing vehicle, the type and direction of the lane change, the presence of lead and lag vehicles, and the lag gap were all significant factors. These findings provide new insights into pre-lane-change behaviour and pave the way for future studies, in part by improving current lane change models. Moreover, the findings have implications for future regulations concerning turn-signal usage by human drivers. Additionally, our results can contribute to the development of algorithms for autonomous vehicles by improving their ability to detect imminent lane changes by surrounding vehicles.
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| 8. |
- Jokhio, Sarang, et al.
(författare)
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Exploring turn signal usage patterns in lane changes: A Bayesian hierarchical modelling analysis of realistic driving data
- 2024
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Ingår i: IET Intelligent Transport Systems. - 1751-9578 .- 1751-956X. ; 18:2, s. 393-408
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Tidskriftsartikel (refereegranskat)abstract
- Using turn signals to convey a driver's intention to change lanes provides a direct and unambiguous way of communicating with nearby drivers. Nonetheless, past research has indicated that drivers may not always use their turn signals before starting a lane change. In this study, realistic driving data are analyzed to investigate turn signal usage during lane changes on highways in and around Gothenburg, Sweden. Turn signal usage is examined and factors that influence it are identified by employing Bayesian hierarchical modelling. The study found that drivers used their turn signal before changing lanes in 60% of cases, after starting the lane change in 33% of cases, and did not use it at all in 7% of cases. The Bayesian hierarchical modelling results indicate that various factors, such as the speed and direction of lane changes and the presence of surrounding vehicles, influence the usage of turn signals. The study concludes that understanding the factors affecting turn signal usage is crucial for improving traffic safety in current and future mixed traffic with autonomous vehicles. The study discusses the implications of findings concerning increasing turn signal compliance through general policy-making, improving existing in-vehicle technologies and including turn signal usage in Pay-As-You-Drive insurances.
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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. |
- Merat, Natasha, et al.
(författare)
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An Overview of Interfaces for Automated Vehicles (inside/outside) (Deliverable D2.1 in the H2020 MSCA ITN project SHAPE-IT)
- 2021
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This Deliverable starts with a short overview of the design principles and guidelines developed for current Human Machine Interfaces (HMIs), which are predominantly developed for manually driven vehicles, or those with a number of Advanced Driver Assistance Systems (ADAS), at SAE Levels 0 and 1 (SAE, 2018). It then provides an overview of how the addition of more capable systems, and the move to higher levels of vehicle automation, is changing the role the human inside an Automated Vehicle (AV), and the ways in which future automated vehicles at higher levels of automation (SAE level 4 and 5) must communicate with other road users, in the absence of an “in charge” human driver. It is argued that such changes in the role of the driver, and more transfer of control to the AV and its different functionalities, means that there will be more emphasis on the roles and responsibilities of HMIs for future AVs. In parallel, the multifaceted nature of these HMI, presented from different locations, both in and outside the vehicles, using a variety of modalities, and engaging drivers in a two-way interaction, means that a new set of design guidelines are required, to ensure that the humans interacting with AVs (inside and outside the vehicle) are not distracted and overloaded, that they remain situation aware and understand the capabilities and limitations of the system, having the right mental model of system capabilities and their responsibilities, as responsible road users, at all times Following a summary of suggested frameworks and design principles which highlight the significant change needed for new AV HMIs, an overview of results from studies investigating human interaction with internal (or iHMIs), and external (or eHMIs), is provided, with examples of new and innovative methods of communication between humans and their vehicles. The Deliverable then provides a summary of the innovative approaches that will be tackled by the ESRs of the project, which focus on factors such as use of AI and AR for future design of more intuitive and transparent HMI, studying how HMI can support the long term interaction of humans with AVs, and the use of neuroergonomic methods for developing safer HMIs. The Deliverable concludes by summarising how each ESR’s project contributes to the development of HMIs for future AVs.
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