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In this work we put forward a predictive trajectory planning framework to help autonomous vehicles plan future trajectories. We develop a partially observable Markov decision process (POMDP) to model this sequential decision making problem, and a deep reinforcement learning solution methodology to learn high-quality policies. The POMDP model utilizes driving scenarios, condensed into graphs, as inputs. More specifically, an input graph contains information on the history trajectory of the subject vehicle, predicted trajectories of other agents in the scene (e.g., other vehicles, pedestrians, and cyclists), as well as predicted risk levels posed by surrounding vehicles to devise safe, comfortable, and energy-efficient trajectories for the subject vehicle to follow. In order to obtain sufficient driving scenarios to use as training data, we propose a simulation framework to generate socially acceptable driving scenarios …