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Abstract
We propose AutoNomos, a distributed and self-regulated approach for the self-organization of a large system of many self-driven, mobile objects. Based on methods for mobile ad-hoc networks using short-distance communication between vehicles, and ideas from distributed algorithms, local data clouds are formed in reaction to specific traffic structures (e.g., traffic jams.) These Hovering Data Clouds (HDCs) are used for forming Organic Information Complexes (OICs) that are functional entities within the traffic flow, hosted by – but independent of – the individual moving vehicles (e.g., a structure detecting, indicating and monitoring the end of a traffic jam that continues to exist in place, even as the involved vehicles are replaced.) Using HDC-based OICs, we develop Adaptable Distributed Strategies (ADSs) for dealing with complex and changing traffic situations. A final goal is the extension to achieve Global Objectives for vehicles and traffic flow. Our project is based on a well-established interdisciplinary cooperation and combines practical know-how from the field of mobile ad-hoc networks with theoretical expertise from a wide range of algorithmic topics.
 
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