论文标题
随机MPC具有双重控制，用于自主驾驶，并具有多模式相互作用的预测
Stochastic MPC with Dual Control for Autonomous Driving with Multi-Modal Interaction-Aware Predictions
论文作者
论文摘要
我们提出了一种自动驾驶的随机MPC（SMPC）方法，其中包含了周围车辆的多模式，相互作用的预测。对于每种模式，车辆运动预测是通过使用未知权重的固定特征的基础描述的对照模型获得的。提出的SMPC公式找到了两个目的的最佳控件：1）通过优化参数化控制法和2）使用Kalman滤波来预测和估计相互作用 - 意识模型中使用的特征权重来降低SMPC的保守性。在一个纵向控制的例子中证明了所提出的方法，并在自动驾驶和周围车辆的预测中不确定。
We propose a Stochastic MPC (SMPC) approach for autonomous driving which incorporates multi-modal, interaction-aware predictions of surrounding vehicles. For each mode, vehicle motion predictions are obtained by a control model described using a basis of fixed features with unknown weights. The proposed SMPC formulation finds optimal controls which serves two purposes: 1) reducing conservatism of the SMPC by optimizing over parameterized control laws and 2) prediction and estimation of feature weights used in interaction-aware modeling using Kalman filtering. The proposed approach is demonstrated on a longitudinal control example, with uncertainties in predictions of the autonomous and surrounding vehicles.
