This paper explores the effects of roadside vegetation and/or cover on the probability of vehicular collisions with deer by identifying and simulating dangerous animal-vehicle interaction scenarios. Deer motion is described by a simplistic model inspired by the literature on deer escape behavior, and a genetic algorithm identifies problematic combinations of model parameter values given a particular vehicle-driver-environment configuration. A nonlinear, planar bicycle model is used to simulate vehicle dynamics. Two simulated driver-assist systems drive the vehicle model in simulation: the first applies brakes to approximate a “forward collision assist” system, and the second combines braking and steering inputs using model predictive control to avoid obstacles. Simulations of these driver-vehicle models interacting with deer are carried out for various configurations of roadside cover geometry. For the configurations explored in this study, the methodology produces recommended safe driving speeds for vehicles employing each driver assist system given a particular road configuration.