MODELING THE INTERACTION OF PEDESTRIANS AND VEHICLES IN UNCONTROLLED TRAFFIC CONDITIONS BASED ON THE STACKELBERG GAME
DOI:
https://doi.org/10.32689/maup.it.2026.2.9Keywords:
pedestrian, vehicle, traffic conflict, unregulated pedestrian crossing, Stackelberg game, game theory, risk assessment, equilibrium strategy, interaction modelingAbstract
The object of the study is the process of interaction between a pedestrian and a vehicle in unregulated traffic conditions in the presence of a potential conflict of their trajectories. The research problem is to determine the rational behavior of road users in situations when the decision-making of one participant directly affects the choice of actions of the other and changes the level of danger. To solve it, a game model of conflict interaction was developed based on the sequential Stackelberg game, in which the pedestrian is the first to form a behavioral signal, and the vehicle determines the appropriate movement strategy. The results obtained consist in determining the equilibrium strategies of the participants taking into account the time before a potential collision, the time after leaving the conflict zone, the minimum distance between the participants and their relative speed. A distinctive feature of the proposed approach is the consideration of the mutual influence of the participants’ decisions, which makes it possible to evaluate not individual actions of a pedestrian or driver, but their coordinated behavior within the same conflict situation and to determine the consequences of changing the chosen strategy for the other participant. Using the example of an unregulated pedestrian crossing, it was found that, under the adopted parameters, the equilibrium strategy is one in which the pedestrian continues to move, and the vehicle reduces speed. The dependence of the equilibrium solution on the initial speed of the car, the distance to the pedestrian, the time to the potential conflict, and the weight coefficients characterizing the significance of safety, speed, and deviation from the trajectory were revealed. The results obtained are explained by the sequential nature of decision-making and the use of backward induction to determine the optimal response of each participant, taking into account the expected actions of the other. The proposed model can be used in practice if information is available about the position, speed, and direction of movement of the participants to assess road conflicts, support decisionmaking, and develop algorithms for intelligent vehicle control, in particular in systems for warning of dangerous approaches and automated selection of the driving mode.
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