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Reinforcement learning (RL) has recently been used for solving challengingdecision-making problems in the context of automated driving. However, one ofthe main drawbacks of the presented RL-based policies is the lack of safetyguarantees, since they strive to reduce the expected number of collisions butstill tolerate them. In this paper, we propose an efficient RL-baseddecision-making pipeline for safe and cooperative automated driving in mergingscenarios. The RL agent is able to predict the current situation and providehigh-level decisions, specifying the operation mode of the low level plannerwhich is responsible for safety. In order to learn a more generic policy, wepropose a scalable RL architecture for the merging scenario that is notsensitive to changes in the environment configurations. According to ourexperiments, the proposed RL agent can efficiently identify cooperative driversfrom their vehicle state history and generate interactive maneuvers, resultingin faster and more comfortable automated driving. At the same time, thanks tothe safety constraints inside the planner, all of the maneuvers are collisionfree and safe.

High-level Decisions from a Safe Maneuver Catalog with Reinforcement Learning for Safe and Cooperative Automated Merging