论文标题
自动化汽车高速公路合并：通过自适应互动混合组MPC进行的运动计划
Automated Vehicle Highway Merging: Motion Planning via Adaptive Interactive Mixed-Integer MPC
论文作者
论文摘要
本文提出了针对自动高速公路合并的新运动计划框架。为了计划合并并预测相邻车辆的运动，自动车辆在退缩的地平线上解决了两种车辆成本的联合优化。可行区域和车道学科的非凸性性质是通过引入整数决策变量来处理模型预测控制（MPC）问题的混合整数二次编程（MIQP）的。此外，自我使用一种反向最佳控制方法来估算相邻车辆成本的重量，通过观察邻居的近期运动并相应地调整其解决方案。我们称此自适应交互式混合整数MPC（AIMPC）。仿真结果显示了提出的框架的有效性。
A new motion planning framework for automated highway merging is presented in this paper. To plan the merge and predict the motion of the neighboring vehicle, the ego automated vehicle solves a joint optimization of both vehicle costs over a receding horizon. The non-convex nature of feasible regions and lane discipline is handled by introducing integer decision variables resulting in a mixed integer quadratic programming (MIQP) formulation of the model predictive control (MPC) problem. Furthermore, the ego uses an inverse optimal control approach to impute the weights of neighboring vehicle cost by observing the neighbor's recent motion and adapts its solution accordingly. We call this adaptive interactive mixed integer MPC (aiMPC). Simulation results show the effectiveness of the proposed framework.
