  Lane change for autonomous vehicles (AVs) is an important but challenging
task in complex dynamic traffic environments. Due to difficulties in guarantee
safety as well as a high efficiency, AVs are inclined to choose relatively
conservative strategies for lane change. To avoid the conservatism, this paper
presents a cooperation-aware lane change method utilizing interactions between
vehicles. We first propose an interactive trajectory prediction method to
explore possible cooperations between an AV and the others. Further, an
evaluation is designed to make a decision on lane change, in which safety,
efficiency and comfort are taken into consideration. Thereafter, we propose a
motion planning algorithm based on model predictive control (MPC), which
incorporates AV's decision and surrounding vehicles' interactive behaviors into
constraints so as to avoid collisions during lane change. Quantitative testing
results show that compared with the methods without an interactive prediction,
our method enhances driving efficiencies of the AV and other vehicles by
14.8$\%$ and 2.6$\%$ respectively, which indicates that a proper utilization of
vehicle interactions can effectively reduce the conservatism of the AV and
promote the cooperation between the AV and others.
