Learning Cooperative Trajectories in Mixed Traffic

未来的交通可能与今天有很大的不同。目前，全球都在努力通过使用自动驾驶汽车来实现交通自动化。但由于在很长一段时间内，将会有不能独立驾驶的车辆，同时也会有喜欢自己驾驶车辆的驾驶员，至少在一个重要的过渡时期，我们将会有自动驾驶车辆和人工驾驶车辆的混合交通。然而，目前大多数关于自动驾驶汽车的研究都集中在每辆汽车的自主性上。在这里提出的项目中，我们研究了如何在混合交通场景中有效地规划和控制大量车辆的轨迹，该项目与优先项目“合作互动汽车”无缝结合。本研究旨在探索混合交通中可证明的安全机动和轨迹集合的学习方法。从理论的角度来看，多智能体的最优调度对我们在这个项目中接近的车辆实时应用构成了挑战。为此，需要应用当前的学习方法，如基于马尔可夫决策过程模型的强化学习，以及使用通用整体质量函数的深度q网络。所开发的方法应在优先计划的仿真环境和真实实验车辆中进行集成、演示和验证。

The traffic of the future is likely to be significantly different from today. Currently, there is widespread global effort to autamate traffic by using self-driving vehicles. But as there will be vehicles for a long time, who can not drive independently and at the same time there will be drivers who like to drive their vehicle themselves, at least for a significant transitional period we will have mixed traffic consisting of self-driving and man-driven vehicles. Most of the current research on self-driving vehicles is focused, however, on the autonomy of each individual vehicle. In the project proposed here, which fits seamlessly into the priority program "Cooperative Interacting Automobiles", we investigate how to efficiently plan and control the trajectories of a number of vehicles in a mixed traffic scenario.The aim of the proposed project is thus to explore learning methods for provably safe maneuver and trajectory collectives in mixed traffic. From a theoretical point of view optimal scheduling for multiple agents constitutes a challenge for real-time applications on a vehicle that we approach in this project. For this purpose, current learning methods shall be applied, such as Reinforcement Learning, which is based on Markov Decision Process models, and deep Q-networks using a common holistic quality function.The developed methods shall be integrated, demonstrated and validated in the simulation environment of the priority program and in real experimental vehicles.