Automated vehicles pose a unique challenge to the safety of vulnerable road users. Research on cyclist-automated vehicle interaction has received relatively little attention compared to pedestrian safety. This exploratory study aims to bridge this gap by identifying cyclist-automated vehicle scenarios and providing recommendations for future research. In this study, we triangulated three sources: a systematic literature review of previous research on cyclists and automated vehicles, group interviews with eight traffic safety and automation experts, and questionnaire data. The resulting scenario collection comprised 20 prototypical scenarios of cyclist-automated vehicle interaction, grouped into four categories based on the road users’ direction of movement: crossing, passing, overtaking, and merging scenarios. The survey results indicated that right-turning vehicles, dooring scenarios, and more complex situations have the highest likelihood of accidents. Passing and merging scenarios are particularly relevant for studying automated vehicle communication solutions since they involve negotiation. Future research should also consider phantom braking and driving styles of vehicles, as well as programming proactive safety behaviours and designing on-vehicle interfaces that accommodate cyclists.

Marie Skłodowska-Curie Actions; Innovative Training Networks (ITN); SHAPE-IT; Grant number 860410

Publication date: 31 December 2023

DOI: 10.1016/j.trip.2023.100986

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The emerging use of automated driving systems introduces novel situations that may affect the safety of vulnerable road users such as cyclists. In this paper, we explain and conceptualise the phenomenon of phantom braking – sudden and unexpected deceleration – in automated vehicles. We apply signal detection theory to interpret phantom braking as a by-product of automated decision-making, with the vehicle favouring the avoidance of accidents at the cost of potentially causing rear-end accidents. To illustrate phantom braking and its effects on cyclists, we used a newly developed cycling simulator. An exploratory measurement conducted with a single cyclist participant revealed a possible complacency effect of the cyclist, with the cyclist’s decision-making mirroring the automated vehicle’s decision-making. The findings provide a testament to using cycling simulators for further exploration of the effects of phantom braking on cyclists.

Marie Skłodowska-Curie Actions; Innovative Training Networks (ITN); SHAPE-IT; Grant number 860410

DOI: 10.54941/ahfe1005212@en

This dissertation explores cyclist-automated vehicle interactions, emphasising developing and integrating human-machine interfaces (HMIs) to enhance cyclist safety and communication. Adopting a cyclist-centric perspective, it recognises cyclists' unique characteristics and communication strategies in shared traffic environments. Using semi-structured interviews, literature reviews, data triangulation, an eye-tracking field experiment, and a cycling simulator study, the research addresses five key research questions, providing qualitative and quantitative insights.

The main contributions of this dissertation include a thorough investigation of cyclists' expectations for future interactions with automated vehicles, highlighting the need for reliable detection by automated vehicles and placing the responsibility for safety on vehicle developers rather than cyclists. The research offers objective data and self-reported insights into cyclist-automated vehicle interactions and evaluates cyclists' ability to visually detect the presence or absence of a driver. Additionally, it introduces 20 scenarios of cyclist-automated vehicle interaction, serving as a resource for safety assessments and HMI research. A comprehensive literature review of existing HMIs for cyclists was conducted, identifying 92 concepts involving vehicles, bicycles, cyclists, and infrastructure.

The dissertation concludes with design recommendations for cyclist-centric HMIs, proposing an omnidirectional on-vehicle external HMI (eHMI) to communicate detection and automated driving mode. This dissertation provides valuable insights for researchers, policymakers, and automated vehicle developers, aiming for the safer, more inclusive, and sustainable urban traffic environments of tomorrow.@en

Tesla's Full Self-Driving Beta (FSD) program introduces technology that extends the operational design domain of standard Autopilot from highways to urban roads. This research conducted 103 in-depth semi-structured interviews with users of Tesla's FSD Beta and standard Autopilot to evaluate the impact on user behavior and perception. It was found that drivers became complacent over time with Autopilot engaged, failing to monitor the system, and engaging in safety-critical behaviors, such as hands-free driving, enabled by weights placed on the steering wheel, mind wandering, or sleeping behind the wheel. Drivers' movement of eyes, hands, and feet became more relaxed with experience with Autopilot engaged. FSD Beta required constant supervision as unfinished technology, which increased driver stress and mental and physical workload as drivers had to be constantly prepared for unsafe system behavior (doing the wrong thing at the worst time). The hands-on wheel check was not considered as being necessarily effective in driver monitoring and guaranteeing safe use. Drivers adapt to automation over time, engaging in potentially dangerous behaviors. Some behavior seems to be a knowing violation of intended use (e.g., weighting the steering wheel), and other behavior reflects a misunderstanding or lack of experience (e.g., using Autopilot on roads not designed for). As unfinished Beta technology, FSD Beta can introduce new forms of stress and can be inherently unsafe. We recommend future research to investigate to what extent these behavioral changes affect accident risk and can be alleviated through driver state monitoring and assistance.

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In future traffic, intelligent user interfaces may aid cyclists in interpreting the behaviour of automated vehicles. Cyclists can be equipped with obstacle-detecting sensors, and an interface could display relevant information or use audible alerts to warn or inform cyclists of other road users' intent and potential hazards. Researching intelligent user interfaces for cyclists is vital for understanding how users can efficiently and safely interact with automated vehicles. This work-in-progress paper presents two studies for developing and testing user interfaces for cyclists in future automated traffic. In the first study, we reanalysed interview data from 30 cyclists, resulting in two interface concepts: the app CycleSafe and an omnidirectional on-vehicle interface capable of communicating cyclist recognition. In the second study, we outline an envisioned experiment to test these two concepts in a naturalistic environment with cyclists and a vehicle emulating automation. We hypothesise that cyclists prefer receiving warning signals over no warnings, prefer early over late warnings, and that auditory signals and visual on-vehicle interfaces will perform better than visual on-bike interfaces.

Marie Skłodowska-Curie Actions; Innovative Training Networks (ITN); SHAPE-IT; Grant number 860410

Publication date: 27 March 2023

DOI: 10.1145/3581754.3584140

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Interaction with vulnerable road users in complex urban traffic environments poses a significant challenge for automated vehicles. Solutions to facilitate safe and acceptable interactions in future automated traffic include equipping automated vehicles and vulnerable road users, such as cyclists, with awareness or notification systems, as well as connecting road users to a network of motorised vehicles and infrastructure. This paper provides a synthesis of the current literature on communication technologies, systems, and devices available to cyclists, including technologies present in the environment and on motorised interaction partners such as vehicles, and discusses the outlook for technology-driven solutions in future automated traffic. The objective is to identify, classify, and count the technologies, systems, and devices that have the potential to aid cyclists in traffic with automated vehicles. Additionally, this study aims to extrapolate the potential benefits of these systems and stimulate discourse on the implications of connected vulnerable road users. We analysed and coded 92 support systems using a taxonomy of 13 variables based on the physical, communicational, and functional attributes of the systems. The discussion frames these systems into four categories: cyclist wearables, on-bike devices, vehicle systems, and infrastructural systems, and highlights the implications of the visual, auditory, motion-based, and wireless modes of communication of the devices. The most common system was cyclist wearables (39%), closely followed by on-bike devices (38%) and vehicle systems (33%). Most systems communicated visually (77%). We suggest that interfaces on motorised vehicles accommodate cyclists with visibility all around the car and incorporate two-way communication. The type of system and the effect of communication modality on performance and safety needs further research, preferably in complex and representative test scenarios with automated vehicles. Finally, our study highlights the ethical implications of connected road users and suggests that the future outlook of transport systems may benefit from a more inclusive and less car-centred approach, shifting the burden of safety away from vulnerable road users and promoting more cyclist-friendly solutions.

Marie Skłodowska-Curie Actions; Innovative Training Networks (ITN); SHAPE-IT; Grant number 860410

Publication date: 6 May 2023

DOI: 10.1016/j.apergo.2023.104043

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Background: Interpreting the subtleness and complexity of vulnerable road user (VRU) behaviour is still a major challenge for automated vehicles (AVs). Solutions for facilitating safe and acceptable interactions in future automated traffic are equipping AVs and VRUs with human-machine interfaces (HMIs) such as awareness and notifications systems, as well as connecting road users to a network of AVs and infrastructure. Research on these solutions largely focuses on pedestrians. However, to ensure the safety of cyclists in future traffic, targeting cyclists as a specific road user group in research is vital. Cyclists sometimes share lanes with vehicles, and have different speeds, movement patterns, and eye-gazing behaviour than pedestrians. Currently, there is no overview of the technologies and solutions for cyclists for enhancing the interaction with AVs. The objectives of the present study are to provide an overview of the communicative technologies, systems, and devices available to cyclists, and evaluate how these solutions meet cyclists’ needs in future automated traffic. Method: To collect relevant academic articles, we performed systematic literature searches in databases such as Scopus, ScienceDirect, and Google Scholar. In addition, we used Google to identify concepts from the industry, including patents and informal concepts. The criterium for the selection of the study sample was set to HMI concepts and communication technologies, where articles not involving cyclists or bicycles were excluded. The study sample was analysed systematically using a taxonomical coding system. Results: We analysed and coded 69 HMI concepts in four systemic categories: cyclist wearables, on-bike devices, vehicle systems, and infrastructural solutions. The concepts are further differentiated according to physical characteristics, intended functionality, modality of communication, and the technology utilised. The concepts are assessed according to the needs and characteristics of cyclists from a human factors’ perspective. The study is ongoing, and the final results are expected First Quarter 2022. Conclusion: The findings from this study provide a synthesis of present literature on AV-cyclist interaction and an overview of the state-of-the-art of the cyclist-specific proposed solutions. By evaluating the HMI concepts according to the characteristics of cyclists, we pave the way for future research on safe and acceptable AV-cyclist interaction.@en

Background: In the future, automated vehicles (AVs) must interpret and adapt to the subtleness and complexity of urban traffic. Interaction with vulnerable road users (VRUs) like pedestrians and cyclists in complex, urban traffic environments is still a major challenge for AVs. While traffic scenarios with pedestrians and AVs are well studied, the field of AV-cyclist interaction is still in early development. With differences in speeds, eye-gaze behaviour, and movement patterns compared to pedestrians, it is crucial to target cyclists as a specific road user group in AV-VRU research. The objectives of this study are to develop realistic scenarios, test criteria, and guidelines for studying AV-cyclist interaction. Method: We are applying a mixed-method approach in this study: A systematic literature review of scenario-specific studies on cyclists and motorised vehicles along with in-depth analyses of cycling accidents and safety-critical situations, focus group interviews with cycling safety and human factors experts, and real-life testing with cyclists. Results: The study is ongoing, and results are expected Third Quarter 2022. From the systematic literature review, we expect to derive the most common cyclist interactive scenarios from cyclist research as well as the relevant situational factors of these scenarios. To evaluate the scenarios and to identify additional technological factors relevant to AVs, the scenarios will be assessed by cycling safety and human factors experts in focus group interviews. The interviews are expected to result in a selection of scenarios and a list of test criteria for each scenario. Validation of the scenarios and test criteria will be evaluated using an instrumented bicycle in real-life traffic. Conclusion: To accommodate the need for more knowledge of AV-cyclist interaction, we will provide a collection of traffic scenarios and methodical guidelines for future research. The findings are particularly relevant for studies on AVs, cyclists, and external human-machine interfaces (HMIs), on-bike HMIs, and infrastructural systems. With a rich description of the situational and technological factors involved in the test scenarios, the scenarios can be utilised to model and predict road user behaviour in future automated traffic.@en

Interpreting the subtleness and complexity of vulnerable road user (VRU) behaviour is still a significant challenge for automated vehicles (AVs). Solutions for facilitating safe and acceptable interactions in future automated traffic include equipping AVs and VRUs with human-machine interfaces (HMl.s), such as awareness and notification systems, and connecting road users to a network of A Vs and infrastructure. The research on these solutions, however, primarily focuses on pedestrians. There is no overview ofthe type of systems or solutions supporting cyclists in future automated traffic. The objective ofthe present study is to synthesise current literature and provide an overview ofthe state-ofthe-art support systems available to cyclists. The aim is to identify, classify, and count the types of communicative technologies, systems, and devices capable of supporting the safety of cyclists in automated traffic. The overall goal is to understand A V-cyclist interaction better, pinpoint knowledge gaps in current literature, and develop strategies for optimising safe and pleasant cycling in future traffic environments with AVs. Marie Skłodowska-Curie Actions; Innovative Training Networks (ITN); SHAPE-IT; Grant number 860410 Publication date: 3 January 2023 DOI: 10.25368/2022.478@en

Cyclists are expected to interact with automated vehicles (AVs) in future traffic, yet we know little about the nature of this interaction and the safety implications of AVs on cyclists. On-bike human–machine interfaces (HMIs) and connecting cyclists to AVs and the road infrastructure may have the potential to enhance the safety of cyclists. This study aimed to identify cyclists’ needs in today's and future traffic, and explore on-bike HMI functionality and the implications of equipping cyclists with devices to communicate with AVs. Semi-structured interviews were conducted with 15 cyclists in Norway and 15 cyclists in the Netherlands. Thematic analysis was used to identify and contextualise the factors of cyclist-AV interaction and on-bike HMIs. From the analysis, seven themes were identified: Interaction, Bicycles, Culture, Infrastructure, Legislation, AVs, and HMI. These themes are diverse and overlap with factors grouped in sub-themes. The results indicated that the cyclists prefer segregated future infrastructure, and in mixed urban traffic, they need confirmation of detection by AVs. External on-vehicle or on-bike HMIs might be solutions to fulfil the cyclists’ need for recognition. However, the analysis suggested that cyclists are hesitant about being equipped with devices to communicate with AVs: Responsibility for safety should lie with AV technology rather than with cyclists. A device requirement might become a barrier to cycling, as bicycles are traditionally cheap and simple, and additional costs might deter people from choosing cycling as a transport mode. Future studies should investigate user acceptance of on-bike HMIs among cyclists on a larger scale to test the findings’ generalisability, and explore other, perhaps more viable solutions than on-bike HMIs for enhancing AV-cyclist interaction.

Marie Skłodowska-Curie Actions; Innovative Training Networks (ITN); SHAPE-IT; Grant number 860410 

Publication date: 29 November 2021

DOI: 10.1016/j.trf.2021.11.013

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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.@en

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.@en