  In this paper, a human-like driving framework is designed for autonomous
vehicles (AVs), which aims to make AVs better integrate into the transportation
ecology of human driving and eliminate the misunderstanding and incompatibility
of human drivers to autonomous driving. Based on the analysis of the real world
INTERACTION dataset, a driving aggressiveness estimation model is established
with the fuzzy inference approach. Then, a human-like driving model, which
integrates the brain emotional learning circuit model (BELCM) with the
two-point preview model, is designed. In the human-like lane-change
decision-making algorithm, the cost function is designed comprehensively
considering driving safety and travel efficiency. Based on the cost function
and multi-constraint, the dynamic game algorithm is applied to modelling the
interaction and decision making between AV and human driver. Additionally, to
guarantee the lane-change safety of AVs, an artificial potential field model is
built for collision risk assessment. Finally, the proposed algorithm is
evaluated through human-in-the-loop experiments on a driving simulator, and the
results demonstrated the feasibility and effectiveness of the proposed method.
