  This paper presents a real-time lane change control framework of autonomous
driving in dense traffic, which exploits cooperative behaviors of other
drivers. This paper focuses on heavy traffic where vehicles cannot change lanes
without cooperating with other drivers. In this case, classical robust controls
may not apply since there is no safe area to merge to without interacting with
the other drivers. That said, modeling complex and interactive human behaviors
is highly non-trivial from the perspective of control engineers. We propose a
mathematical control framework based on Model Predictive Control (MPC)
encompassing a state-of-the-art Recurrent Neural network (RNN) architecture. In
particular, RNN predicts interactive motions of other drivers in response to
potential actions of the autonomous vehicle, which are then systematically
evaluated in safety constraints. We also propose a real-time heuristic
algorithm to find locally optimal control inputs. Finally, quantitative and
qualitative analysis on simulation studies are presented to illustrate the
benefits of the proposed framework.
