Model Predictive Control for Integrated Motion Planning and Control of Automated Vehicles

自动车辆集成运动规划和控制的模型预测控制

• 批准号: 460891204
• 负责人: Professor Dr. Georg Schildbach
• 金额: --
• 依托单位: Institut für Medizinische Elektrotechnik (IME)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/460891204?language=en
• 关键词: Model; Predictive; Control; Integrated; Motion

The main goal of this project is to improve the technology of Model Predictive Control (MPC) for automated vehicles. MPC has been identified as a promising approach for the trajectory tracking problem in several research projects. Its key advantage is the holistic formulation of nonlinear multi-variable control problems with constraints, which is versatile and intuitive. However, MPC has not found access into industrial production yet, for several well-known reasons. Most importantly, MPC is computationally rather expensive and it requires a high degree of implementation skills. Therefore this project aims at mitigating these drawbacks, while further strengthening the advantages of MPC. In particular, the focus will be on the approach of Scenario-based MPC (SCMPC), on which the applicant has worked intensively over the past years. This work shall be continued in order to strengthen and expand the fundamental theory of SCMPC. Moreover, the scope of MPC shall be extended to comprise the two basic tasks of trajectory tracking control and motion planning. This leads to a simplification of the underlying software architecture and a reduction of the required interfaces compared to other approaches. The particular strength of MPC of incorporating constraints can be used to explicitly derive safety guarantees for the closed-loop system. To avoid unnecessary conservatism in the behavior of the automated vehicle, the future behavior of the surrounding traffic has to be interpreted and anticipated. To this end, the framework of SCMPC uses scenarios for the prediction of the behavior of other traffic participants. The main idea is that a bundle of scenarios (or particles) can be used to express the likely future behavior of other agents as well as its inherent uncertainty. This approach is computationally efficient, intuitive, and easy to implement, because it can draw directly from empirical driving data. Explicit safety guarantees can be derived by the theory on scenario-based optimization.In the second part of this project, a user-friendly toolbox will be developed that facilitates the implementation of MPC on a wide range of hardware systems. This toolbox will significantly reduce the computational burden of MPC, as it builds on efficient state-of-the-art algorithms that are specifically tailored for the task of integrated vehicle motion planning and control. The toolbox includes a code generator that can be operated via a graphical user interface (GUI). Thus it abstracts most of the difficulties of the implementation process and standalone C code can be produced for the controller, which can be integrated and tested on most embedded platforms.In summary, this project will expand the current state of technology of MPC with respect to automated driving, and it will significantly simplify its industrial application.

该项目的主要目标是改进自动驾驶车辆的模型预测控制技术。在一些研究项目中，MPC被认为是一种很有前途的轨迹跟踪方法。它的主要优点是对带约束的非线性多变量控制问题进行整体表述，具有通用性和直观性。然而，由于几个众所周知的原因，MPC尚未进入工业生产。最重要的是，MPC在计算上相当昂贵，并且需要高度的实现技能。因此，本项目旨在减轻这些缺点，同时进一步加强MPC的优势。特别是，重点将放在基于场景的MPC （SCMPC）的方法上，申请人在过去几年中一直在此方面进行深入研究。这项工作将继续下去，以加强和拓展常务委员会的基础理论。同时，将MPC的范围扩大到包含轨迹跟踪控制和运动规划两个基本任务。与其他方法相比，这导致了底层软件体系结构的简化和所需接口的减少。结合约束的MPC的特殊强度可以用来明确地推导闭环系统的安全保证。为了避免自动驾驶车辆的行为出现不必要的保守性，必须对周围交通的未来行为进行解释和预测。为此，SCMPC框架使用场景来预测其他交通参与者的行为。主要思想是，一束场景（或粒子）可以用来表达其他代理的可能的未来行为以及其固有的不确定性。这种方法计算效率高，直观，易于实现，因为它可以直接从经验驾驶数据中提取。基于场景的优化理论可以推导出明确的安全保证。在该项目的第二部分，将开发一个用户友好的工具箱，以促进MPC在各种硬件系统上的实现。该工具箱将大大减少MPC的计算负担，因为它建立在专门为集成车辆运动规划和控制任务量身定制的高效最先进算法的基础上。该工具箱包括一个可以通过图形用户界面（GUI）操作的代码生成器。因此，它抽象了实现过程中的大部分困难，并且可以为控制器生成独立的C代码，这些代码可以在大多数嵌入式平台上集成和测试。综上所述，该项目将扩展MPC在自动驾驶方面的技术现状，并将大大简化其工业应用。