This paper presents a 2D cellular automaton model for simulating pedestrian dynamics. The model incorporates a floor field that mediates long-range interactions between pedestrians, allowing for collective effects and self-organization, such as lane formation in counterflow through a corridor. The floor field, which can be discrete or continuous, is subject to diffusion and decay, and is modified by pedestrian motion. The model uses a parallel update procedure and has a maximal velocity of 1, making it computationally efficient. It is shown that the floor field can effectively simulate pedestrian behavior without requiring explicit intelligence, as pedestrians follow a virtual trace rather than a chemical one. The model is applied to simulate pedestrian evacuation in a large room with reduced visibility and to study lane formation in a corridor. The model is compared to other cellular automaton and continuous models, and is found to be able to reproduce collective phenomena such as lane formation and oscillations at doors. The model is also shown to be computationally efficient and applicable to a wide range of traffic flow problems. The key feature of the model is the introduction of the floor field, which allows for the simulation of long-range interactions through local interactions with the floor field. The model is validated through simulations of pedestrian behavior in various scenarios, including evacuation and lane formation. The results show that the model can reproduce empirical observations of pedestrian dynamics and is a promising tool for studying complex crowd behavior.This paper presents a 2D cellular automaton model for simulating pedestrian dynamics. The model incorporates a floor field that mediates long-range interactions between pedestrians, allowing for collective effects and self-organization, such as lane formation in counterflow through a corridor. The floor field, which can be discrete or continuous, is subject to diffusion and decay, and is modified by pedestrian motion. The model uses a parallel update procedure and has a maximal velocity of 1, making it computationally efficient. It is shown that the floor field can effectively simulate pedestrian behavior without requiring explicit intelligence, as pedestrians follow a virtual trace rather than a chemical one. The model is applied to simulate pedestrian evacuation in a large room with reduced visibility and to study lane formation in a corridor. The model is compared to other cellular automaton and continuous models, and is found to be able to reproduce collective phenomena such as lane formation and oscillations at doors. The model is also shown to be computationally efficient and applicable to a wide range of traffic flow problems. The key feature of the model is the introduction of the floor field, which allows for the simulation of long-range interactions through local interactions with the floor field. The model is validated through simulations of pedestrian behavior in various scenarios, including evacuation and lane formation. The results show that the model can reproduce empirical observations of pedestrian dynamics and is a promising tool for studying complex crowd behavior.