  To develop driving automation technologies for human, a human-centered
methodology should be adopted for ensured safety and satisfactory user
experience. Automated lane change decision in dense highway traffic is
challenging, especially when considering the personalized preferences of
different drivers. To fulfill human driver centered decision algorithm
development, we carry out driver-in-the-loop experiments on a
6-Degree-of-Freedom driving simulator. Based on the analysis of the lane change
data by drivers of three specific styles,personalization indicators are
selected to describe the driver preferences in lane change decision. Then a
deep reinforcement learning (RL) approach is applied to design human-like
agents for automated lane change decision, with refined reward and loss
functions to capture the driver preferences.The trained RL agents and benchmark
agents are tested in a two-lane highway driving scenario, and by comparing the
agents with the specific drivers at the same initial states of lane change, the
statistics show that the proposed algorithm can guarantee higher consistency of
lane change decision preferences. The driver personalization indicators and the
proposed RL-based lane change decision algorithm are promising to contribute in
automated lane change system developing.
