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的目标是开发一种分布式和自我调节的方法，用于组织许多自驱动的移动对象的大型系统。基于使用车辆之间短距离通信的ad-hoc网络方法和分布式算法的思想，本地数据云可以根据特定的交通结构（例如，拥堵）形成。这些悬停数据云（HDCs）用于形成有机信息复合物（oic），这些有机信息复合物是交通流中的功能实体，由但独立于单个移动车辆托管（例如，用于检测、指示和监控持续存在的交通堵塞结构，即使涉及的车辆被替换）。使用基于hdc的oic，我们开发了可适应分布式策略（ads）来处理复杂和不断变化的交通情况。最终目标是扩展到实现车辆和交通流量的全球目标。我们的项目建立在一个良好的跨学科合作的基础上，并结合了移动自组织网络领域的实际知识和来自广泛算法主题的理论专业知识。虽然前三年我们的六年项目主要是处理当地的现象，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，并强调整体场景

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