  This paper presents an online smooth-path lane-change control framework. We
focus on dense traffic where inter-vehicle space gaps are narrow, and
cooperation with surrounding drivers is essential to achieve the lane-change
maneuver. We propose a two-stage control framework that harmonizes Model
Predictive Control (MPC) with Generative Adversarial Networks (GAN) by
utilizing driving intentions to generate smooth lane-change maneuvers. To
improve performance in practice, the system is augmented with an adaptive
safety boundary and a Kalman Filter to mitigate sensor noise. Simulation
studies are investigated in different levels of traffic density and
cooperativeness of other drivers. The simulation results support the
effectiveness, driving comfort, and safety of the proposed method.
