A driving assistance method for a road vehicle, the driving assistance method comprising the steps of: detecting, within a traffic scene including the road vehicle, a set of road users and, for each road user of the set of road users, a current state including a current speed and a current position; assigning a dynamic behavioral model, from among a finite plurality of behavioral models, to each road user of the set of road users, wherein said dynamic behavioral model has at least one dynamic feature for taking into account a state of a road user other than the road user to which the dynamic behavioral model is assigned; probabilistically estimating by a data processor, a belief state for each road user of the set of road users, using the dynamic behavioral model assigned to each road user of the set of road users, wherein the belief state comprises a set of alternative subsequent states and corresponding probabilities, each alternative subsequent state including a speed and a position; determining a risk of collision of the road vehicle with a road user of the set of road users, based on the probabilistically estimated belief state of each road user of the set of road users; and outputting a driver warning signal and/or executing an avoidance action if the risk of collision exceeds a predetermined threshold; wherein the belief state is probabilistically estimated for each road user of the set of road users by the data processor performing the steps of: estimating a probability of each action from a set of alternative subsequent actions for each road user of the set of road users, using the dynamic behavioral model assigned to each road user of the set of road users and taking into account at least a previously calculated occupancy probability distribution of the set of road users; calculating each alternative subsequent state resulting from each action of the set of alternative subsequent actions for the corresponding road user. detecting, within a traffic scene including the road vehicle, a set of road users and, for each road user of the set of road users, a current state including a current speed and a current position; assigning a dynamic behavioral model, from among a finite plurality of behavioral models, to each road user of the set of road users, wherein said dynamic behavioral model has at least one dynamic feature for taking into account a state of a road user other than the road user to which the dynamic behavioral model is assigned; probabilistically estimating by a data processor, a belief state for each road user of the set of road users, using the dynamic behavioral model assigned to each road user of the set of road users, wherein the belief state comprises a set of alternative subsequent states and corresponding probabilities, each alternative subsequent state including a speed and a position; determining a risk of collision of the road vehicle with a road user of the set of road users, based on the probabilistically estimated belief state of each road user of the set of road users; and outputting a driver warning signal and/or executing an avoidance action if the risk of collision exceeds a predetermined threshold; wherein the belief state is probabilistically estimated for each road user of the set of road users by the data processor performing the steps of: estimating a probability of each action from a set of alternative subsequent actions for each road user of the set of road users, using the dynamic behavioral model assigned to each road user of the set of road users and taking into account at least a previously calculated occupancy probability distribution of the set of road users; calculating each alternative subsequent state resulting from each action of the set of alternative subsequent actions for the corresponding road user. estimating a probability of each action from a set of alternative subsequent actions for each road user of the set of road users, using the dynamic behavioral model assigned to each road user of the set of road users and taking into account at least a previously calculated occupancy probability distribution of the set of road users; calculating each alternative subsequent state resulting from each action of the set of alternative subsequent actions for the corresponding road user.