According to crash data reports, most collisions between cyclists and motorized vehicles occur at unsignalized intersections (where no traffic lights regulate vehicle priority). In the era of automated driving, it is imperative for automated vehicles to ensure the safety of cyclists, especially at these intersections. In other words, to safely interact with cyclists, automated vehicles need models that can describe how cyclists cross and yield at intersections. So far, only a few studies have modeled the interaction between cyclists and motorized vehicles at intersections, and none of them have explored the variations in interaction outcomes based on the type of drivers involved. In this study, we compare non-professional drivers (represented by passenger car drivers) and professional drivers (truck and taxi drivers). We also introduce a novel application of game theory by comparing logit and game theoretic models’ analyses of the interactions between cyclists and motorized vehicles, leveraging naturalistic data. Interaction events were extracted from a trajectory dataset, and cyclists’ non-kinematic cues were extracted from videos and incorporated into the interaction events’ data. The modeling outputs showed that professional drivers are less likely to yield to cyclists than non-professional drivers. Furthermore, the behavioral game theoretic models outperformed the logit models in predicting cyclists’ crossing decisions.

Understanding driver-pedestrian interactions at unsignalized locations has gained additional importance due to recent advancements in vehicle automation. Naturalistic observations can only provide correlational data, of limited value for understanding and modeling the mechanisms underlying road user interaction. Therefore, controlled studies in virtual reality (VR) are an important complement, but conventional methods only accommodate a single human participant. Recently, there has been a surge in interest in studying interactions in VR, by means of distributed simulation, involving multiple human participants. However, there is a lack of validation of this method. Here, we provide a validation study, focusing on a distributed vehicle-pedestrian interaction setup, where pairs of one driver and one pedestrian interacted under various kinematic conditions in a connected virtual environment. To test the validity of the distributed simulation, we used a naturalistic dataset collected in the same UK city, at similar locations, and compared the observed behavior between the two settings. Our results indicate a good relative validity of the simulator study, where road users showed similar non-verbal communication behavior in both datasets. As an additional means of validation, we also leveraged a set of game theoretic models that were developed based on the simulator studies, and found that when applied to the naturalistic dataset, we obtained similar (although not identical) model selection results. This suggests that distributed simulation can also be useful for development of computational models of interaction. Overall, the findings suggest that distributed simulation can be a highly valuable tool for studying and modeling road user interactions.

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.