Increasing the response time of emergency vehicles (EVs) could lead to an immeasurable loss of property and life. On this account, tactical decision making for EVs' microscopic control remains an indispensable issue to be improved. In this paper, a rule-based avoiding strategy (AS) is devised, that common vehicles (CVs) in the prioritized zone ahead of EV should accelerate or change their lane to avoid it. Besides, a novel DQN method with speed-adaptive compact state space (SC-DQN) is put forward to fit in EVs' high-speed features and generalize in various road topologies. Afterward, the execution of AS feedback to the input of SC-DQN so that they joint organically as a combinational method. The following approach reveals that deep reinforcement learning (DRL) could complement rule-based AS in generalization, and on the contrary, the rule-based AS could complement the stability of DRL, and their combination could lead to less response time, lower collision rate, and smoother trajectory.

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