Modelling vehicle-pedestrian interactions at unsignalised locations: Road users may not play the Nash equilibrium

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.