A Situation-Aware Adaptation Framework for Intelligent Transportation Systems

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.