  Merging into the highway from the on-ramp is an essential scenario for
automated driving. The decision-making under the scenario needs to balance the
safety and efficiency performance to optimize a long-term objective, which is
challenging due to the dynamic, stochastic, and adversarial characteristics.
The Rule-based methods often lead to conservative driving on this task while
the learning-based methods have difficulties meeting the safety requirements.
In this paper, we propose an RL-based end-to-end decision-making method under a
framework of offline training and online correction, called the Shielded
Distributional Soft Actor-critic (SDSAC). The SDSAC adopts the policy
evaluation with safety consideration and a safety shield parameterized with the
barrier function in its offline training and online correction, respectively.
These two measures support each other for better safety while not damaging the
efficiency performance severely. We verify the SDSAC on an on-ramp merge
scenario in simulation. The results show that the SDSAC has the best safety
performance compared to baseline algorithms and achieves efficient driving
simultaneously.
