Learning Cooperative Trajectories in Mixed Traffic

学习混合交通中的合作轨迹

• 批准号: 273350361
• 负责人: Professor Dr. Wolfram Burgard
• 金额: --
• 依托单位: Institut für Mess- und Regelungstechnik (MRT)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/273350361?language=en
• 关键词: Learning; Cooperative; Trajectories; Mixed; Traffic

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.

未来的交通很可能与今天大不相同。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。但是，由于在很长一段时间内会有车辆无法独立驾驶，同时也会有司机喜欢自己驾驶车辆，至少在一个重要的过渡期内，我们将有自动驾驶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，它无缝地融入优先程序“协同互动自动化”，我们研究如何有效地规划和控制的轨迹的车辆在混合交通scenaries.The建议项目的目的是探索学习方法，证明安全的机动和轨迹集体在混合交通。从理论的角度来看，多个代理的最佳调度构成了一个挑战，我们在这个项目中的方法的车辆上的实时应用。为此，应应用当前的学习方法，例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。应在优先计划的模拟环境和真实的实验车辆中集成、演示和验证开发的方法。