GROKO-Plan: GRaph-based, Optimal and COoperative Trajectory planning for Interacting Automobiles

本计画旨在发展一种以图形为基础的互动载具最佳合作轨迹规划方法。本文的研究内容包括分布式协同轨迹规划方法的理论分析和实际实现，并将在不同的测试平台上对协同交叉口场景进行验证，分布式协同轨迹规划的主要挑战是规划轨迹的可靠性（I），车载优化器的实时性（II），以及车辆之间可实现的通信努力（III）。第一个挑战是，无冲突轨道是强制性的。第二个挑战是由于高维非凸优化问题，因为它们发生时，许多道路使用者必须考虑在内。第三个挑战是由合作的繁荣，即解决一个车辆上的合作优化问题，其他道路使用者的优化问题的一部分必须交换，为了满足这些挑战，本项目提出了减少轨迹规划库，包括机动和修剪原语，用于合作轨迹规划。轨迹规划，然后进行分布式模型预测控制（DMPC）减少合作图。鲁棒DMPC方法考虑了感知和预测的不确定性。由此，可以生成无冲突轨迹（I）。总之，规划方法提供的轨迹是一系列的机动和修剪原语。这大大减少了搜索空间，并实现了实时优化（II）。 与现有的基于图的方法相比，新的轨迹规划方法在单个车辆上协同工作，并考虑到相邻车辆的目标和限制。在传达之前缩小图表对我们的方法至关重要。它简化了优化问题的复杂性（II）和通信的努力成为可以接受的（III）。在这个项目中的新算法的模拟验证是基于模拟平台，它已经在SPP内开发的第一个资金阶段。特别是，开发的算法，合作的感知和预测，以及存档的交通情况将被使用。反过来，仿真平台将扩展本项目的结果。我们将利用评估指标和基准系统，例如CommonRoad，这些指标和基准系统已经并将在SPP过程中开发。

This project aims at developing a graph-based planning method for optimal cooperative trajectories for interacting vehicles. Our research includes the theoretical analysis as well as the practical implementation of a distributed cooperative trajectory planning method, which will be validated for a cooperative traffic intersection scenario on different test platforms.The major challenges of distributed cooperative trajectory planning are dependability of planned trajectories (I), real-time compliance of the optimizer on board of the vehicles (II), and a realizable communication effort between vehicles (III). The first challenge arises since conflict-free trajectories are mandatory. The second challenge is due to high-dimensional non-convex optimization problems, as they occur when many road users have to be taken into account. The third challenge is caused by the thrive for cooperation, i.e. to solve the cooperative optimization problem on one vehicle, parts of the optimization problems of other road users have to be exchanged.In order to meet these challenges, this project proposes to use reduced trajectory planning libraries, comprised of maneuvers and trim primitives, for cooperative trajectory planning. Trajectory planning is then performed by distributed model predictive control (DMPC) working on reduced cooperative graphs. Uncertainty in perception and prediction is considered in robust DMPC methods. Thereby, conflict free trajectories can be generated (I). All together, the planning method provides trajectories which are sequences of maneuvers and trim primitives. This considerably reduces the search space and enables real-time optimization (II). In contrast to existing graph-based approaches, the novel trajectory planning method on an individual vehicle works cooperatively und takes into account objectives and restrictions of neighboring vehicles. Reducing the graphs before communicating them is crucial to our approach. It simplifies the complexity of the optimization problems (II) and the communication effort becomes acceptable (III).The simulative validation of the novel algorithms within this project is based on the simulation platform which has been developed within the SPP during the first funding phase. In particular, developed algorithms for cooperative perception and prediction as well as the archive of traffic scenarios will be used. In turn, the simulation platform will be extended by the results of this project. We will make use of evaluation metrics and benchmark systems, e.g. CommonRoad, which have been and will be developed within the course of the SPP.