In recent years, nonlinear model predictive control (NMPC) has been extensively used for solving automotive motion control and planning tasks. In order to formulate the NMPC problem, different coordinate systems can be used with different advantages. We propose and compare formulations for the NMPC related optimization problem, involving a Cartesian and a Frenet coordinate frame (CCF/ FCF) in a single nonlinear program (NLP). We specify costs and collision avoidance constraints in the more advantageous coordinate frame, derive appropriate formulations and compare different obstacle constraints. With this approach, we exploit the simpler formulation of opponent vehicle constraints in the CCF, as well as road aligned costs and constraints related to the FCF. Comparisons to other approaches in a simulation framework highlight the advantages of the proposed approaches.
翻译:近年来,非线性模型预测控制(NMPC)被广泛用于解决汽车运动控制和规划任务,为了解决NMPC问题,可以使用不同的协调系统,具有不同的优势,我们提出并比较NMPC相关优化问题的配方,包括一个笛卡尔和Frenet协调框架(CCF/FCF),在单一的非线性方案(NLP)中,我们具体规定了更有利的协调框架中的成本和避免碰撞的制约因素,提出了适当的配方,比较了不同的障碍限制。我们利用这一方法,利用国家合作框架中较简单的对手车辆限制的配方,以及与FCFF有关的道路协调成本和限制。在模拟框架中与其他方法的比较突出了拟议办法的优势。