A significant challenge for autonomous vehicles today is learning to drive with other drivers. This stems from the need to accurately model others drivers' actions, which is especially difficult because of how many unique driving styles can exist. We present a meta learning approach: By treating the experience of driving with others as tasks, the deep learning model creates a unique representation for each experience. During test time, the model can quickly adapt to unseen driving styles with only a few updates. We present promising initial results: the meta learning model outperforms reinforcement learning baselines, even accounting for exposure to new drivers.