  Decision-making is critical for lane change in autonomous driving.
Reinforcement learning (RL) algorithms aim to identify the values of behaviors
in various situations and thus they become a promising pathway to address the
decision-making problem. However, poor runtime safety hinders RL-based
decision-making strategies from complex driving tasks in practice. To address
this problem, human demonstrations are incorporated into the RL-based
decision-making strategy in this paper. Decisions made by human subjects in a
driving simulator are treated as safe demonstrations, which are stored into the
replay buffer and then utilized to enhance the training process of RL. A
complex lane change task in an off-ramp scenario is established to examine the
performance of the developed strategy. Simulation results suggest that human
demonstrations can effectively improve the safety of decisions of RL. And the
proposed strategy surpasses other existing learning-based decision-making
strategies with respect to multiple driving performances.
