Augmented Reality (AR) technology could be utilised to assist pedestrians in navigating safely through traffic. However, whether potential users would understand and use such AR solutions is currently unknown. Nine novel AR interfaces for pedestrian-vehicle communication, previously developed using an experience-based design method, were evaluated through an online questionnaire study completed by 992 respondents in Germany, the Netherlands, Norway, Sweden, and the United Kingdom. The AR indicated whether it was safe to cross the road in front of an approaching automated vehicle. Each interface was rated for its intuitiveness and convincingness, aesthetics, and usefulness. Moreover, comments were collected for qualitative analysis. The results indicated that interfaces that employed traditional design elements from existing traffic, and head-up displays, received the highest ratings overall. Statistical results also showed that there were no significant effects of country, age, and gender on interface acceptance. Thematic analysis of the textual comments offered detail on each interface design's stronger and weaker points, and revealed unintended effects of certain designs. In particular, some of the interfaces were commented on as being dangerous or scary, or were criticised that they could be misinterpreted in that they signal that something is wrong with the vehicle, or that they could occlude the view of the vehicle. The current findings highlight the limitations of experience-based design, and the importance of applying legacy design principles and involving target users in design and evaluation. Future research should be conducted in scenarios in which pedestrians actually interact with approaching vehicles.Marie Skłodowska-Curie Actions; Innovative Training Networks (ITN); SHAPE-IT; Grant number 860410Publication date: 21 February 2023DOI: 10.1016/j.trf.2023.02.005@en

Nine AR interfaces designed using an experience- based, and theoretically informed design approach, were presented in an online questionnaire for user evaluation. Statistical analysis of presented measures, and the computation of an overall composite score revealed a preference towards traditional and familiar traffic elements.@en

One of the current challenges of automation is to have highly automated vehicles (HAVs) that communicate effectively with pedestrians and react to changes in pedestrian behaviour, to promote more trustable HAVs. However, the details of how human drivers and pedestrians interact at unsignalised crossings remain poorly understood. We addressed some aspects of this challenge by replicating vehicle–pedestrian interactions in a safe and controlled virtual environment by connecting a high fidelity motion-based driving simulator to a CAVE-based pedestrian lab in which 64 participants (32 pairs of one driver and one pedestrian) interacted with each other under different scenarios. The controlled setting helped us study the causal role of kinematics and priority rules on interaction outcome and behaviour, something that is not possible in naturalistic studies. We also found that kinematic cues played a stronger role than psychological traits like sensation seeking and social value orientation in determining whether the pedestrian or driver passed first at unmarked crossings. One main contribution of this study is our experimental paradigm, which permitted repeated observation of crossing interactions by each driver-pedestrian participant pair, yielding behaviours which were qualitatively in line with observations from naturalistic studies.@en

One of the current challenges of automation is to have highly automated vehicles (HAVs) that communicate effectively with pedestrians and react to changes in pedestrian behaviour, to promote more trustable HAVs. However, the details of how human drivers and pedestrians interact at unsignalised crossings remain poorly understood. We addressed some aspects of this challenge by replicating vehicle–pedestrian interactions in a safe and controlled virtual environment by connecting a high fidelity motion-based driving simulator to a CAVE-based pedestrian lab in which 64 participants (32 pairs of one driver and one pedestrian) interacted with each other under different scenarios. The controlled setting helped us study the causal role of kinematics and priority rules on interaction outcome and behaviour, something that is not possible in naturalistic studies. We also found that kinematic cues played a stronger role than psychological traits like sensation seeking and social value orientation in determining whether the pedestrian or driver passed first at unmarked crossings. One main contribution of this study is our experimental paradigm, which permitted repeated observation of crossing interactions by each driver-pedestrian participant pair, yielding behaviours which were qualitatively in line with observations from naturalistic studies.@en

Highly automated vehicles (HAVs) will need to interact with pedestrians in a safe and efficient way. Thus, investigating and modeling vehicle-pedestrian interactions at uncontrolled locations is essential to ensure safety and acceptance of these vehicles. Controlled studies are a valuable tool for these scenarios where all the tasks are not possible to be done in the real world and where some variables should be controlled with high accuracy for the development of models of human behavior. In this paper, a game-theoretic model was tested using data from a distributed simulator study. The study was conducted by connecting a desktop driving simulator to a CAVE-based pedestrian lab, providing a safe environment for testing the model’s ability to capture the gap acceptance behavior of pedestrians when interacting with a Human-Driven (HD) or an Automated Vehicle (AV). The results showed that, overall, the model could capture pedestrian behavior well and the pedestrians had lower crossing probabilities in front of the AV. This was seemingly due to differences in vehicle kinematics. Further analysis of the pedestrians’ data revealed the importance of given instructions to the participants in these types of studies. Lessons learned through this study were also used to suggest further ideas on how to design controlled studies for game-theoretic modelling.@en

Highly automated vehicles (HAVs) will need to interact with pedestrians in a safe and efficient way. Thus, investigating and modeling vehicle-pedestrian interactions at uncontrolled locations is essential to ensure safety and acceptance of these vehicles. Controlled studies are a valuable tool for these scenarios where all the tasks are not possible to be done in the real world and where some variables should be controlled with high accuracy for the development of models of human behavior. In this paper, a game-theoretic model was tested using data from a distributed simulator study. The study was conducted by connecting a desktop driving simulator to a CAVE-based pedestrian lab, providing a safe environment for testing the model’s ability to capture the gap acceptance behavior of pedestrians when interacting with a Human-Driven (HD) or an Automated Vehicle (AV). The results showed that, overall, the model could capture pedestrian behavior well and the pedestrians had lower crossing probabilities in front of the AV. This was seemingly due to differences in vehicle kinematics. Further analysis of the pedestrians’ data revealed the importance of given instructions to the participants in these types of studies. Lessons learned through this study were also used to suggest further ideas on how to design controlled studies for game-theoretic modelling.@en

Highly automated vehicles (HAVs) will need to interact with pedestrians in a safe and efficient way. Thus, investigating and modeling vehicle-pedestrian interactions at uncontrolled locations is essential to ensure safety and acceptance of these vehicles. Controlled studies are a valuable tool for these scenarios where all the tasks are not possible to be done in the real world and where some variables should be controlled with high accuracy for the development of models of human behavior. In this paper, a game-theoretic model was tested using data from a distributed simulator study. The study was conducted by connecting a desktop driving simulator to a CAVE-based pedestrian lab, providing a safe environment for testing the model’s ability to capture the gap acceptance behavior of pedestrians when interacting with a Human-Driven (HD) or an Automated Vehicle (AV). The results showed that, overall, the model could capture pedestrian behavior well and the pedestrians had lower crossing probabilities in front of the AV. This was seemingly due to differences in vehicle kinematics. Further analysis of the pedestrians’ data revealed the importance of given instructions to the participants in these types of studies. Lessons learned through this study were also used to suggest further ideas on how to design controlled studies for game-theoretic modelling.@en

Highly automated vehicles (HAVs) will need to interact with pedestrians in a safe and efficient way. Thus, investigating and modeling vehicle-pedestrian interactions at uncontrolled locations is essential to ensure safety and acceptance of these vehicles. Controlled studies are a valuable tool for these scenarios where all the tasks are not possible to be done in the real world and where some variables should be controlled with high accuracy for the development of models of human behavior. In this paper, a game-theoretic model was tested using data from a distributed simulator study. The study was conducted by connecting a desktop driving simulator to a CAVE-based pedestrian lab, providing a safe environment for testing the model’s ability to capture the gap acceptance behavior of pedestrians when interacting with a Human-Driven (HD) or an Automated Vehicle (AV). The results showed that, overall, the model could capture pedestrian behavior well and the pedestrians had lower crossing probabilities in front of the AV. This was seemingly due to differences in vehicle kinematics. Further analysis of the pedestrians’ data revealed the importance of given instructions to the participants in these types of studies. Lessons learned through this study were also used to suggest further ideas on how to design controlled studies for game-theoretic modelling.@en

Highly automated vehicles (HAVs) will need to interact with pedestrians in a safe and efficient way. Thus, investigating and modeling vehicle-pedestrian interactions at uncontrolled locations is essential to ensure safety and acceptance of these vehicles. Controlled studies are a valuable tool for these scenarios where all the tasks are not possible to be done in the real world and where some variables should be controlled with high accuracy for the development of models of human behavior. In this paper, a game-theoretic model was tested using data from a distributed simulator study. The study was conducted by connecting a desktop driving simulator to a CAVE-based pedestrian lab, providing a safe environment for testing the model’s ability to capture the gap acceptance behavior of pedestrians when interacting with a Human-Driven (HD) or an Automated Vehicle (AV). The results showed that, overall, the model could capture pedestrian behavior well and the pedestrians had lower crossing probabilities in front of the AV. This was seemingly due to differences in vehicle kinematics. Further analysis of the pedestrians’ data revealed the importance of given instructions to the participants in these types of studies. Lessons learned through this study were also used to suggest further ideas on how to design controlled studies for game-theoretic modelling.@en

Highly automated vehicles (HAVs) will need to interact with pedestrians in a safe and efficient way. Thus, investigating and modeling vehicle-pedestrian interactions at uncontrolled locations is essential to ensure safety and acceptance of these vehicles. Controlled studies are a valuable tool for these scenarios where all the tasks are not possible to be done in the real world and where some variables should be controlled with high accuracy for the development of models of human behavior. In this paper, a game-theoretic model was tested using data from a distributed simulator study. The study was conducted by connecting a desktop driving simulator to a CAVE-based pedestrian lab, providing a safe environment for testing the model’s ability to capture the gap acceptance behavior of pedestrians when interacting with a Human-Driven (HD) or an Automated Vehicle (AV). The results showed that, overall, the model could capture pedestrian behavior well and the pedestrians had lower crossing probabilities in front of the AV. This was seemingly due to differences in vehicle kinematics. Further analysis of the pedestrians’ data revealed the importance of given instructions to the participants in these types of studies. Lessons learned through this study were also used to suggest further ideas on how to design controlled studies for game-theoretic modelling.@en

Highly automated vehicles (HAVs) will need to interact with pedestrians in a safe and efficient way. Thus, investigating and modeling vehicle-pedestrian interactions at uncontrolled locations is essential to ensure safety and acceptance of these vehicles. Controlled studies are a valuable tool for these scenarios where all the tasks are not possible to be done in the real world and where some variables should be controlled with high accuracy for the development of models of human behavior. In this paper, a game-theoretic model was tested using data from a distributed simulator study. The study was conducted by connecting a desktop driving simulator to a CAVE-based pedestrian lab, providing a safe environment for testing the model’s ability to capture the gap acceptance behavior of pedestrians when interacting with a Human-Driven (HD) or an Automated Vehicle (AV). The results showed that, overall, the model could capture pedestrian behavior well and the pedestrians had lower crossing probabilities in front of the AV. This was seemingly due to differences in vehicle kinematics. Further analysis of the pedestrians’ data revealed the importance of given instructions to the participants in these types of studies. Lessons learned through this study were also used to suggest further ideas on how to design controlled studies for game-theoretic modelling.@en

Highly automated vehicles (HAVs) will need to interact with pedestrians in a safe and efficient way. Thus, investigating and modeling vehicle-pedestrian interactions at uncontrolled locations is essential to ensure safety and acceptance of these vehicles. Controlled studies are a valuable tool for these scenarios where all the tasks are not possible to be done in the real world and where some variables should be controlled with high accuracy for the development of models of human behavior. In this paper, a game-theoretic model was tested using data from a distributed simulator study. The study was conducted by connecting a desktop driving simulator to a CAVE-based pedestrian lab, providing a safe environment for testing the model’s ability to capture the gap acceptance behavior of pedestrians when interacting with a Human-Driven (HD) or an Automated Vehicle (AV). The results showed that, overall, the model could capture pedestrian behavior well and the pedestrians had lower crossing probabilities in front of the AV. This was seemingly due to differences in vehicle kinematics. Further analysis of the pedestrians’ data revealed the importance of given instructions to the participants in these types of studies. Lessons learned through this study were also used to suggest further ideas on how to design controlled studies for game-theoretic modelling.@en