利用上下文中间件提高车载智能管理系统的态势感知能力

W. Wibisono, A. Zaslavsky, Sea Ling
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引用次数: 6

摘要

智能汽车、传感器和通信技术的普及导致了旨在提高驾驶安全性和效率的车对车(V2V)应用的出现。这些应用程序的重要需求是能够为驱动程序提供不显眼的支持,以及应用程序能够在没有显式驱动程序干预的情况下适应环境中不断变化的情况。本文提出了一种新的V2V环境下的情境推理方法。我们基于上下文空间对上下文和情景建模,并将该模型与Dempster-Shafer组合规则相结合,进行情景推理。在基于折扣规则的融合机制中考虑了各个信息源的可靠性。我们将这种方法应用到一个上下文中间件框架中,该框架旨在促进上下文和情景推理,为V2V环境中的协作应用提供可靠的支持,并讨论原型的实现和实验问题
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Improving situation awareness for intelligent on-board vehicle management system using context middleware
The proliferation of intelligent vehicle, sensor and communication technology has led to the emergence of vehicle-to-vehicle (V2V) applications that aim to increase safety and efficiency of driving. The important requirements of these applications are the capabilities to provide unobtrusive support to the driver and the application capability to adapt to changing situations in the environment without having explicit driver intervention. This paper proposes a new approach for context and situation reasoning in V2V environment. We model context and situations based on Context Spaces and integrate the model with Dempster-Shafer rule of combination for situation reasoning. We also incorporate reliability of each information source in the fusion mechanism based on discount rule. We apply this approach to a context middleware framework that aims to facilitate context and situation reasoning to provide reliable support for cooperative applications in V2V environment and discuss the implementation and experimentation issues of the prototype
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