Self-organizing and self-regulating coordination of a large swarm of self-navigating autonomous vehicles as occuring in traffic

我们的项目AUTONOMOS的目标是开发一个分布式和自我调节的方法，用于组织一个大型系统，许多自驱动的，移动的对象。基于用于使用车辆之间的短距离通信的自组织网络的方法以及来自分布式算法的思想，响应于特定的交通结构（例如，果酱）。这些悬停数据云HDC用于形成有机信息复合体（OIC），其是交通流内的功能实体，由单独的移动车辆托管但独立于单独的移动车辆（例如，用于检测、指示和监视即使在所涉及的车辆被替换时仍继续存在的交通堵塞的结构）。使用基于HDC的OIC，我们开发了适应性分布式策略（ADS），以处理复杂和不断变化的交通情况。最终目标是实现车辆和交通流量的全球目标。我们的项目是基于一个完善的跨学科合作，并结合了从移动的ad-hoc网络领域的实际知识与理论专长“从广泛的算法主题。虽然我们的六年项目的前三年主要处理当地的现象，如交通流量及其改善，但下半年的目标是将我们的工作扩展到更复杂的概念，方法和结构，重点是整体场景

The objective of our project AUTONOMOS is the development a distributed and self-regulated aproach for the organization of a large system many self-driven, mobile objects. Based on methods for ad-hoc networks using short-distance comunication betwen vehicles, and ideas from distributed algorithms, local data clouds are formed in reaction to specific trafic structures (e.g., jams). These Hovering Data Clouds HDCs) are used for forming Organic Information Complexes (OICs) that are functional entities within the trafic flow, hosted by—but independent of—individual moving vehicles (e.g., for detecting, indicating and monitoring the structure of a trafic jam that continues to exist in place, even as the involved vehicles axe replaced.) Using HDC-based OICs, we develop Adaptable Distributed Strategies (ADSs) for dealing with complex and changing trafic situations. A final goal is thextension to achieve Global Objectives for vehicles and trafic flow. Our project is based on a wel-established interdisciplinary coperation and combines practical know-how from the field of mobile ad-hoc networks with theoretical expertise'from a wide range of algorithmic topics. While the firsthre years our six-year project dealt with largely local phenomena like trafic flow and their improvement, the goal for second half is thextension of our work to even more complex concepts, methods, and structures, with an emphasis on overal scenarios