Understanding older adults' overall expectations about automated vehicles (AVs) is crucial for inclusive designs. The work-in-progress presents an exploratory study based on semi-structured interviews with 27 older adults in the Netherlands. A thematic analysis revealed an open-minded attitude towards AVs, optimism for improved safety, and pragmatic concerns about reliability. Participants expected AVs to be "well-behaved", delivering safe, predictable, and socially considerate driving styles. Participants also showed a desire for AVs to be communicative, providing feedback to reduce uncertainties. The findings provide implications for inclusive AV designs.

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 can only accommodate a single human participant. Recently, there has been some 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 U.K. 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 nonverbal 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. The findings suggest 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.

As we move towards a future with Automated Vehicles (AVs) incorporated in the current traffic system, it is crucial to understand driver-pedestrian interaction, in order to enhance AV design and optimization. Previous research in this area, which has primarily used naturalistic observations or single-actor virtual reality simulations, has been limited by its inability to draw causal conclusions, also due to a lack of real human–human interactions. Our study addresses these limitations by employing a high-fidelity distributed simulation setup that links drivers in a motion-based simulator with pedestrians in a CAVE-based environment. This method allows for the examination of real-time and reciprocal interactions across a range of road-crossing scenarios. Using thirty-two pairs of drivers and pedestrians, we investigated how different factors, such as the presence of zebra crossings and varying time gaps of the approaching vehicle, influence driver behaviour and pedestrian crossing decisions. The effect of drivers’ control of the vehicle during such crossings (e.g., braking behaviour and lateral deviation) on pedestrians’ crossing decisions were also analysed. We found that the distribution of drivers’ average deceleration values were bimodal, where drivers either markedly yielded to pedestrians, or continued in their path, with very few instances of intermediate behaviour. We also found that pedestrian decisions were seemingly influenced by the different braking strategies adopted by the driver, with pedestrians crossing before the vehicles in response to soft and early, or late and hard braking, while late and soft braking often resulted in the vehicle passing first. We also observed a slight lateral movement of the vehicle away from pedestrians when drivers were not yielding, but more of a lateral deviation towards them when yielding. This may be because drivers subconsciously transfer their walking interaction habits to their driving behaviour, to avoid a collision with pedestrians. Finally, our results showed a stronger influence of these kinematic cues on pedestrian crossing decisions, when compared to zebra crossings. As well as highlighting the value of a novel approach for investigating vehicle–pedestrian interactions, this study illustrates how vehicle cues can assist pedestrian decisions, adding new knowledge in the development of human-like behaviour for future AVs.

User comfort in higher-level Automated Vehicles (AVs, SAE Level 4+) is crucial for public acceptance. AV driving styles, characterised by vehicle kinematic and proxemic factors, affect user comfort, with “human-like” driving styles expected to provide natural feelings. We investigated a) how the kinematic and proxemic factors of an AV's driving style affect users' evaluation of comfort and naturalness, and b) how the similarities between automated and users' manual driving styles affect user evaluation. Using a motion-based driving simulator, participants experienced three Level 4 automated driving styles: two human-like (defensive, aggressive) and one machine-like. They also manually drove the same route. Participants rated their comfort and naturalness of each automated controller, across twenty-four varied UK road sections. We calculated maximum absolute values of the kinematic and proxemic factors affecting the AV's driving styles in longitudinal, lateral, and vertical directions, for each road section, to characterise the automated driving styles. The Euclidean distance between AV and manual driving styles, in terms of kinematic and proxemic factors, was calculated to characterise the human-like driving style of the AV. We used mixed-effects models to examine a) the effect of AV's kinematic and proxemic factors on the evaluation of comfort and naturalness, and b) how similarities between manual and automated driving styles affected the evaluation. Results showed significant effects of lateral and rotational kinematic factors on comfort and naturalness, with longitudinal kinematic factors having a less prominent effect. Similarities in vehicle metrics, such as speed, longitudinal jerk, lateral offset, and yaw, between manual and automated driving styles, enhanced user comfort and naturalness. This research facilitates an understanding of how control features of AVs affect user experience, contributing to the design of user-centred controllers and better acceptance of higher-level AVs.

The driving style of an automated vehicle (AV) needs to be comfortable to encourage the broad acceptance and use of this newly emerging transport mode. However, current research provides limited knowledge about what influences comfort, how this concept is described, and how it is measured. This knowledge is especially lacking when comfort is linked to the AV's driving styles. This paper presents results from an online workshop with nine experts, all with hands-on experience of AVs and a long track record of research in this context. Using online tools, experts were invited to introduce concepts they considered relevant to comfort/discomfort in currently available modes of transport which offer a ride (taxi/bus/train) to users and compare these to the concepts used to define comfort and discomfort in AVs. Results showed that a wide range of terms were used to describe user comfort and discomfort for both modes. Although all terms used for existing vehicles were found to apply to AVs, additional terms were proposed for determining comfort/discomfort of AVs. For example, to enhance comfort in AVs, designers should consider good communication channels, as well as ensuring that the AV's capabilities match users’ expectations. Results also revealed that more terms were used, overall, to define discomfort, and that a comfortable ride in AVs is not just about mitigating discomfort. New concepts specific to AVs were also revealed when considering what increases their discomfort, such as whether riders’ safety and privacy are affected, or if they feel in control. Experts’ input from the workshop was used to enhance and expand a simple conceptual framework, explaining how AV driving styles, as well as other, non-driving-related factors, affect user comfort. It is hoped that this framework provides a more comprehensive list of the concepts affecting user comfort, also allowing more accurate measurement of the concept. As well as allowing for a more accurate comparison between empirical studies measuring comfort in AVs, this study will facilitate the design of more comfortable and acceptable automated driving for future vehicles.

Introduction: Augmented reality (AR) has been increasingly studied in transportation, particularly for drivers and pedestrians interacting with automated vehicles (AVs). Previous research evaluated AR interfaces using online video-based questionnaires but lacked human-subject research in immersive environments. This study examined if prior online evaluations of nine AR interfaces could be replicated in an immersive virtual environment and if AR interface effectiveness depends on pedestrian attention allocation. 

Methods: Thirty participants completed 120 trials in a CAVE-based simulator with yielding and non-yielding AVs, rating the interface’s intuitiveness and crossing the road when they felt safe. To emulate visual distraction, participants had to look into an attention-attractor circle that disappeared 1 s after the interface appeared. 

Results: The results showed that intuitiveness ratings from the current CAVE-based study and the previous online study correlated strongly (r ≈ 0.90). Head-locked interfaces and familiar designs (augmented traffic lights, zebra crossing) yielded higher intuitiveness ratings and quicker crossing initiations than vehicle-locked interfaces. Vehicle-locked interfaces were less effective when the attention-attractor was on the environment’s opposite side, while head-locked interfaces were relatively unaffected by attention-attractor position. 

Discussion: In conclusion, this ‘AR in VR’ study shows strong congruence between intuitiveness ratings in a CAVE-based study and online research, and demonstrates the importance of interface placement in relation to user gaze direction.

When humans share space in road traffic, as drivers or as vulnerable road users, they draw on their full range of communicative and interactive capabilities. Much remains unknown about these behaviors, but they need to be captured in models if automated vehicles are to coexist successfully with human road users. Empirical studies of human road user behavior implicate a large number of underlying cognitive mechanisms, which taken together are well beyond the scope of existing computational models. Here, we note that for all of these putative mechanisms, computational theories exist in different subdisciplines of psychology, for more constrained tasks. We demonstrate how these separate theories can be generalized from abstract laboratory paradigms and integrated into a computational framework for modeling human road user interaction, combining Bayesian perception, a theory of mind regarding others’ intentions, behavioral game theory, long-term valuation of action alternatives, and evidence accumulation decision-making. We show that a model with these assumptions—but not simpler versions of the same model—can account for a number of previously unexplained phenomena in naturalistic driver–pedestrian road-crossing interactions, and successfully predicts interaction outcomes in an unseen data set. Our modeling results contribute to demonstrating the real-world value of the theories from which we draw, and address calls in psychology for cumulative theory-building, presenting human road use as a suitable setting for work of this nature. Our findings also underscore the formidable complexity of human interaction in road traffic, with strong implications for the requirements to set on development and testing of vehicle automation.

Current research on vehicle-pedestrian interactions focuses on the reaction of one actor other than the interaction of two actors, and considering the impact of the real-time behaviour of both actors on each other. To address this issue, the current study replicated a natural vehicle-pedestrian interaction to the virtual environment by connecting a high-fidelity driving simulator to a CAVE-based pedestrians' simulator. Behaviours from both actors in response to each other were observed indifferent situations including two crossing locations and five time gaps. The proposed method enabled simultaneous interaction in a controlled and safe environment as well as provided implications for future AV design.

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.

Recent developments in vehicle automation require simulations of human-robot interactions in the road traffic context, which can be achieved by computational models of human behavior such as game theory. Game theory provides a good insight into road user behavior by considering agents' interdependencies. However, it is still unclear whether conventional game theory is suitable for modeling vehicle-pedestrian interactions at unsignalized locations or if more complex models like behavioral game theory are needed. Hence, we compared four game-theoretic models based on two different payoff formulations and two solving algorithms, to answer this question. Unlike the most previous studies that employed naturalistic datasets to test and validate such models, this study utilized a distributed simulation dataset to test and compare the models. The study was conducted by connecting a CAVE-based pedestrian simulator to a motion-based driving simulator to replicate the traffic scenarios for 32 pedestrian-driver pairs. The findings demonstrated that there is a high variability between participant pairs' behaviors. Our proposed behavioral game-theoretic model outperformed other models in predicting the interaction outcome. This translates to a decrease by 70% and 67% in the root mean squared error (RMSE) when compared to the baseline model, for marked and unmarked crossings, respectively. The model can also predict which interaction will take the longest time to resolve. According to our results, road users cannot be expected to behave in line with the Nash equilibrium of conventional game theory that underscores the complexity of human behavior with implications for the testing and development of automated vehicles.

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 860410

Publication date: 21 February 2023

DOI: 10.1016/j.trf.2023.02.005

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.

Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving. Pedestrian crossing behavior is influenced by various interaction factors, including time to arrival, pedestrian waiting time, the presence of zebra crossing, and the properties and personality traits of both pedestrians and drivers. However, these factors have not been fully explored for use in predicting interaction outcomes. In this paper, we use machine learning to predict pedestrian crossing behavior including pedestrian crossing decision, crossing initiation time (CIT), and crossing duration (CD) when interacting with vehicles at unsignalized crossings. Distributed simulator data are utilized for predicting and analyzing the interaction factors. Compared with the logistic regression baseline model, our proposed neural network model improves the prediction accuracy and F1 score by 4.46% and 3.23%, respectively. Our model also reduces the root mean squared error (RMSE) for CIT and CD by 21.56% and 30.14% compared with the linear regression model. Additionally, we have analyzed the importance of interaction factors, and present the results of models using fewer factors. This provides information for model selection in different scenarios with limited input features.

As the introduction of automated vehicles (AVs) into road traffic accelerates, establishing user acceptance is increasingly crucial. User comfort, largely influenced by the AVs' driving styles, is one of the essential factors influencing acceptance. This video submission provides a methodological overview of a qualitative interview study, which used a Wizard-of-Oz method to investigate participants' comfort levels during automated driving on real roads. By understanding the specific comfort experiences of both older and younger users, we can inform the design process for AVs, thereby enhancing user experience and facilitating broader acceptance of technology across a more diverse and inclusive demographic spectrum.

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.

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.

The L3Pilot project tested SAE Level 3 (L3) conditionally automated driving functions addressing driving and travel behavior, impacts on safety, efficiency, environment and socio-economics, and user acceptance. To investigate individual variance in acceptance of conditionally automated cars, an online survey was performed among 18,631 respondents from 17 countries evaluating differences in age, gender, knowledge about the functionality of conditionally automated cars, awareness, information consumption behavior, and expected benefits of conditionally automated cars. Respondents were divided into Enthusiasts, Neutrals, and Sceptics differing in a high, moderate, and low acceptance of conditionally automated cars, respectively. Enthusiasts, Neutrals, and Sceptics differed most with regard to the expected benefits in the productive use of travel time, comfort, and safety of conditionally automated cars. Enthusiasts were male, younger, more knowledgeable about conditionally automated cars, more aware of automated cars, and more likely to receive information about automated cars from different sources, expecting improvements in the productive use of travel time, comfort, and safety due to conditionally automated cars. All groups were most knowledgeable about the lane keeping behavior of conditionally automated cars and least knowledgeable about the operation of conditionally automated cars in dedicated operational design domains. The results indicate that the communication and marketing of automated cars should create a realistic image of the capabilities and limitations of conditionally automated cars where user education programs should be harmonized to calibrate expectations and educate the public.

The impact of automated vehicles (AV) on pedestrians’ crossing behavior has been the topic of some recent studies, but findings are still scarce and inconclusive. The aim of this study is to determine whether the drivers’ presence and apparent attentiveness in a vehicle influences pedestrians’ crossing behavior, perceived behavioral control, and perceived risk, in a controlled environment, using a Head-mounted Display in an immersive Virtual Reality study. Twenty participants took part in a road-crossing experiment. The VR environment consisted of a single lane one-way road with car traffic approaching from the right-hand side of the participant which travelled at 30 kmph. Participants were asked to cross the road if they felt safe to do so. The effect of three driver conditions on pedestrians’ crossing behavior were studied: Attentive driver, distracted driver, and no driver present. Two vehicles were employed with a fixed time gap (3.5 s and 5.5 s) between them to study the effects of time gaps on pedestrians’ crossing behavior. The manipulated vehicle yielded to the pedestrians in half of the trials, stopping completely before reaching the pedestrian's position. The crossing decision, time to initiate the crossing, crossing duration, and safety margin were measured. The main findings show that the vehicle's motion cues (i.e. the gap between the vehicles, and the yielding behavior of the vehicle) were the most important factors affecting pedestrians’ crossing behavior. Therefore, future research should focus more on investigating how AVs should behave while interacting with pedestrians. Distracted driver condition leads to shorter crossing initiation time but the effect was small. No driver condition leads to smaller safety margin. Findings also showed that perceived behavioral control was higher and perceived risk was significantly lower when the driver appeared attentive. Given that drivers will be allowed to do other tasks while AVs are operating in the future, whether explicit communication will be needed in this situation should be further investigated.

Automated driving research over the past decades has mostly focused on highway environments. Recent technological developments have drawn researchers and manufacturers to look ahead at introducing automated driving in cities. The current position paper examines this challenge from the viewpoint of scientific experts. Sixteen Human Factors researchers were interviewed about their personal perspectives on automated vehicles (AVs) and the interaction with VRUs in the future urban environment. Aspects such as smart infrastructure, external human-machine interfaces (eHMIs), and the potential of augmented reality (AR) were addressed during the interviews. The interviews showed that the researchers believed that fully autonomous vehicles will not be introduced in the coming decades and that intermediate levels of automation, specific AV services, or shared control will be used instead. The researchers foresaw a large role of smart infrastructure and expressed a need for AV-VRU segregation, but were concerned about corresponding costs and maintenance requirements. The majority indicated that eHMIs will enhance future AV-VRU interaction, but they noted that implicit communication will remain dominant and advised against text-based and instructive eHMIs. AR was commended for its potential in assisting VRUs, but given the technological challenges, its use, for the time being, was believed to be limited to scientific experiments. The present expert perspectives may be instrumental to various stakeholders and researchers concerned with the relationship between VRUs and AVs in future urban traffic.

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

Publication date: 4 January 2021

DOI: 10.1016/j.trip.2020.100293

The future urban environment may consist of mixed traffic in which pedestrians interact with automated vehicles (AVs). However, it is still unclear how AVs should communicate their intentions to pedestrians. Augmented reality (AR) technology could transform the future of interactions between pedestrians and AVs by offering targeted and individualized communication. This paper presents nine prototypes of AR concepts for pedestrian-AV interaction that are implemented and demonstrated in a real crossing environment. Each concept was based on expert perspectives and designed using theoretically-informed brainstorming sessions. Prototypes were implemented in Unity MARS and subsequently tested on an unmarked road using a standalone iPad Pro with LiDAR functionality. Despite the limitations of the technology, this paper offers an indication of how future AR systems may support future pedestrian-AV interactions.

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

Publication date: 20 September 2021

DOI: 10.1145/3409118.3475149