This research considers the problem of predicting whether a car will suffer a collision in the time period 10-20 seconds in the future. We formulate this task as policy evaluation in a MDP with a high-dimensional, continuous state space, and a reward function dominated by rare events (collisions). We then demonstrate that simulated data and domain adaptation models can be used to improve prediction performance on real-world data.