  Model Predictive Control (MPC) has been widely applied to the motion planning
of autonomous vehicles. An MPC-controlled vehicle is required to predict its
own trajectories in a finite prediction horizon according to its model. Beyond
this, the vehicle should also incorporate the prediction of the trajectory of
its nearby vehicles, or target vehicles (TVs) into its decision-making. The
conventional trajectory prediction methods, such as the constant-speed-based
ones, are too trivial to accurately capture the potential collision risks. In
this report, we propose a novel MPC-based motion planning method for an
autonomous vehicle with a set of risk-aware constraints. These constraints
incorporate the predicted trajectory of a TV learned using a
deep-learning-based method. A recurrent neural network (RNN) is used to predict
the TV's future trajectory based on its historical data. Then, the predicted TV
trajectory is incorporated into the optimization of the MPC of the ego vehicle
to generate collision-free motion. Simulation studies are conducted to showcase
the prediction accuracy of the RNN model and the collision-free trajectories
generated by the MPC.
