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In this paper we present a test bed for multiagent control systems in road traffic management. As the complexity of traffic control on a network grows it becomes more difficult to coordinate the actions of the large number of heterogeneous traffic management instruments that are available in the network. One way of handling this complexity is to divide the coordination problem into smaller coherent subproblems that can be solved with a minimum of interaction. Multiagent systems can aid in the distribution of the problem (over the various agents that comprise the multiagent system) and facilitate the coordination of the activities of these agents when required. In the literature no consensus exists about the best configuration of the traffic managing multiagent system and how the activities of the agents that comprise the multiagent system should be coordinated. The decomposition of a problem into various subproblems is an active field of research in the world of distributed artificial intelligence. This paper starts out with a survey of the approaches as they are reported in the literature. Subsequently the test bed is introduced and the modules it is comprised of. Finally an application is presented that illustrates some of the research the test bed has made possible.

In this paper we present a test bed for multi-agent control systems in road traffic management. As the complexity of traffic control on a network grows it becomes more difficult to coordinate the actions of the large number of heterogeneous traffic management instruments that are available in the network. One way of handling this complexity is to divide the coordination problem into smaller coherent subproblems that can be solved with a minimum of interaction. Multi-agent systems can aid in the distribution of the problem (over the various agents that comprise the multi-agent system) and facilitate the coordination of the activities of these agents when required. In the literature no consensus exists about the best configuration of the traffic managing multi-agent system and how the activities of the agents that comprise the multi-agent system should be coordinated. The decomposition of a problem into various subproblems is an active field of research in the world of distributed artificial intelligence. This paper starts out with a survey of the approaches as they are reported in the literature. Subsequently the test bed is introduced and the modules it is comprised of. Finally an application is presented that illustrates some of the research the test bed has made possible.

This article introduces the objectives and structure of the European research project DESERVE that is co-funded by the ARTEMIS-JU and national funding bodies. The project started in September 2012 with a duration of 3 years. The project aims to establish a new embedded SW and HW design by using a more efficient development process (including the enabling general platform concept and tool chain) in order to overcome challenges in reducing component costs and development time of future ADAS functions for modern vehicles. Both the process and the platform concept will be demonstrated with innovative ADAS functions in 3 passenger cars, 1 truck and 1 motorcycle.Embedded hardware and software units have been developed for improving electronic horizon band of vehicles by detecting objects in front. Moreover, driver/motorcycle rider awareness is analysed by monitoring his/her actions online. The systems need to be robust and reliable in different environment conditions (night time, rain, etc.). The DESERVE platform distinguishes three layers of intelligence: perception, application and intervention&warning control. The demonstrators will be based on software development tools from Elektrobit (ADTF) and Intempora (RTmaps). These tools are used to create 10 innovative ADAS applications as part of an integral ADAS development platform, following a new design and development process. Since the project is highly application oriented, the requirements have been adapted mainly from the ISO 26262 standard and the AUTOSAR framework which ensures compatibility with the existing automotive software environment.