  It is expected that many human drivers will still prefer to drive themselves
even if the self-driving technologies are ready. Therefore, human-driven
vehicles and autonomous vehicles (AVs) will coexist in a mixed traffic for a
long time. To enable AVs to safely and efficiently maneuver in this mixed
traffic, it is critical that the AVs can understand how humans cope with risks
and make driving-related decisions. On the other hand, the driving environment
is highly dynamic and ever-changing, and it is thus difficult to enumerate all
the scenarios and hard-code the controllers. To face up these challenges, in
this work, we incorporate a human decision-making model in reinforcement
learning to control AVs for safe and efficient operations. Specifically, we
adapt regret theory to describe a human driver's lane-changing behavior, and
fit the personalized models to individual drivers for predicting their
lane-changing decisions. The predicted decisions are incorporated in the safety
constraints for reinforcement learning in training and in implementation. We
then use an extended version of double deep Q-network (DDQN) to train our AV
controller within the safety set. By doing so, the amount of collisions in
training is reduced to zero, while the training accuracy is not impinged.
