Behavioral decision making is an important aspect of autonomous vehicles (AV). In this work, we propose a behavior planning structure based on hierarchical reinforcement learning (HRL) which is capable of performing autonomous vehicle planning tasks in simulated environments with multiple sub-goals. In this hierarchical structure, the network is capable of 1) learning one task with multiple sub-goals simultaneously; 2) extracting attentions of states according to changing subgoals during the learning process; 3) reusing the well-trained network of sub-goals for other tasks with the same sub-goals. A hybrid reward mechanism is designed for different hierarchical layers in the proposed HRL structure. Compared to traditional RL methods, our algorithm is more sample-efficient, since its modular design allows reusing the policies of sub-goals across similar tasks for various transportation scenarios. The results show that the proposed method converges to an optimal policy faster than traditional RL methods.