Abstract: We present a novel algorithm to model density-dependent behaviours in crowd simulation. Previous work has shown that density is a key factor in governing how pedestrians adapt their behaviour. This paper specifically examines, through analysis of real pedestrian data, how density affects how agents control their rate of change of bearing angle with respect to one another. We extend upon existing synthetic vision based approaches to local collision avoidance and generate pedestrian trajectories that more faithfully represent how real people avoid each other. Our approach is capable of producing realistic human behaviours, particularly in dense, complex scenarios where the amount of time for agents to make decisions is limited.