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.

翻译：模型预测控制（MPC）已广泛应用于自动驾驶车辆的运动规划中。基于MPC的车辆需要根据其模型在有限预测时域内预测自身轨迹。此外，车辆还应将附近车辆（即目标车辆）的轨迹预测纳入其决策过程。传统的轨迹预测方法（如基于恒定速度的方法）过于简单，难以准确捕捉潜在的碰撞风险。本文提出了一种基于MPC的自主车辆运动规划方法，该方法引入了一组具有风险感知的约束条件。这些约束整合了通过深度学习方法学习得到的目标车辆预测轨迹。采用循环神经网络（RNN）基于目标车辆的历史数据预测其未来轨迹。随后，将预测的目标车辆轨迹纳入自车MPC的优化过程中，以生成无碰撞运动轨迹。通过仿真研究验证了RNN模型的预测精度以及MPC生成的无碰撞轨迹。