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A Situation-Aware Adaptation Framework for Intelligent Transportation Systems

机译：智能交通系统的情境感知适应框架

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摘要

A transportation system is usually well-defined and operates based on a specific model defined during system design. However, the system can interact with different objects from its environment at runtime and needs to guarantee its functional and timing behavior even in the presence of adverse or failure situations through self-adaptation. Traditionally, techniques such as design analysis and testing are performed during the system design and development stage. During the adaptation process, the transportation system needs to provide assurance such that it is safe and schedulable. We present an adaptation framework, which guarantees the functional and timing behavior of the system in different situations by creating a knowledge base from mining the video stream of the monitored environment. The knowledge base provides information on system interactions with external objects, constraints imposed on the system due to interactions, and characterizes the runtime behavior. We guarantee the timing behavior by evaluating the constraints and their effects on the performance of the system. The experimental analysis of our work demonstrates that the situation-aware adaptation framework can significantly improve system performance by reducing scheduling overload and response time.
机译：运输系统通常定义明确，并基于系统设计过程中定义的特定模型进行操作。但是，系统可以在运行时与其环境中的不同对象进行交互，并且即使在出现不利或故障情况时，也需要通过自适应来保证其功能和计时行为。传统上，在系统设计和开发阶段执行诸如设计分析和测试之类的技术。在适应过程中，运输系统需要提供保证，以确保其安全性和可调度性。我们提出了一个适应框架，该框架通过挖掘受监视环境的视频流来创建知识库，从而保证了系统在不同情况下的功能和定时行为。知识库提供有关系统与外部对象的交互作用，由于交互作用而对系统施加的约束的信息，并描述运行时行为。通过评估约束及其对系统性能的影响，我们保证了时序行为。我们工作的实验分析表明，情境感知适应框架可以通过减少调度过载和响应时间来显着提高系统性能。

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