Interaction between road users is a societally important special case of human interaction, and a better understanding of such interactions is a key missing enabler for wide deployment of automated vehicles. Empirical studies implicate a variety of cognitive mechanisms, but these are studied and modeled in separate subfields of psychology. Here, we show how a range of these existing computational theories can be integrated into a single modeling framework, and demonstrate that to reproduce a set of well-established empirical phenomena in driver-pedestrian interaction, substantial theoretical unification of this nature is needed. Our results demonstrate the feasibility and value of large-scale integration of psychological theory, and underscore the formidable complexity of road user interaction, with strong implications for both mechanistic and machine-learned approaches to vehicle automation.
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.
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.
Modelling human-robot interaction in the road traffic context is an evolving yet understudied area. Recent developments in vehicle automation require simulations of such interactions, which can be achieved by computational models of human behaviour such as game theory. Game theory is a modelling paradigm that provides a good insight into road user behaviour by considering agents’ interdependencies. However, it is still unclear whether conventional game theory is suitable for modelling vehicle-pedestrian interactions at unsignalised locations or if more complex models like behavioural game theory are needed. Hence, we compared four game-theoretic models based on two different payoff formulations and two solving algorithms obtained from the conventional and behavioural game theory literature, to answer this question. Data from 32 pedestrian-driver pairs, who interacted with each other in a distributed simulator study was used to test and validate the models. The study was conducted by connecting a CAVE-based pedestrian simulator to a motion-based driving simulator providing a safe and controlled environment to replicate the traffic scenarios. The findings demonstrated that there is a high variability between participant pairs’ behaviours. Our proposed behavioural game-theoretic model outperformed other models in predicting the interaction outcome as a function of kinematics and crossing type. The model can also predict which interaction will take the longest time to resolve, something that traditional models such as logit and conventional game theory are incapable of. According to our results, road users cannot be expected to behave in line with the Nash equilibrium of conventional game theory which underscores the complexity of human behaviour with implications for the testing and development of automated vehicles.
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