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

本项目旨在开发一种基于图的规划方法，用于相互作用的车辆的最优合作轨迹。我们的研究包括理论分析和分布式协同轨迹规划方法的实际实现，并将在不同的测试平台上对协同交通路口场景进行验证。分布式协同轨迹规划的主要挑战是规划轨迹的可靠性(I)、优化器在车辆上的实时遵从性（II）以及车辆之间可实现的通信努力（III）。第一个挑战是由于无冲突轨迹是强制性的。第二个挑战是由于高维非凸优化问题，因为它们发生在必须考虑许多道路用户的情况下。第三个挑战是由于合作的繁荣造成的，即为了解决一辆车上的合作优化问题，必须交换其他道路使用者的部分优化问题。为了应对这些挑战，本项目提出使用由机动和修剪原语组成的精简轨迹规划库进行协同轨迹规划。然后利用分布式模型预测控制（DMPC）在约简合作图上进行轨迹规划。鲁棒DMPC方法考虑了感知和预测中的不确定性。因此，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）。与现有的基于图的路径规划方法相比，该方法在单个车辆上协同工作，并考虑了相邻车辆的目标和限制。对我们的方法来说，在传递图形之前简化它们是至关重要的。它简化了优化问题的复杂性（II），并且通信工作变得可以接受（III）。该项目中新算法的模拟验证基于SPP在第一资助阶段开发的模拟平台。特别是，将使用已开发的协作感知和预测算法以及交通场景存档。反过来，仿真平台将通过本项目的结果进行扩展。我们将利用评估指标和基准系统，例如在SPP过程中已经并将开发的CommonRoad。

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.