The goal of this CAREER project is to design and develop an integrated scheduling framework for networked multicore-based control systems in smart vehicles. The advanced functionalities (such as stability control and collision avoidance) in modern vehicles impose high computational demand on their electronic control systems, which can be powered by multicore processors to mitigate their increasing complexity. However, the existing scheduling theory and techniques have fallen short of supporting such networked multicore-based control systems, especially considering the human-related factors and dynamic environments of smart vehicles.
This project undertakes a comprehensive study of resource management techniques and scheduling algorithms to efficiently schedule various real-time applications and effectively utilize the computation power in networked multicore-based smart vehicle control systems. First, a hierarchical control architecture with a simplified high-level master controller is being investigated to achieve accurate situational awareness and ensure prompt response. Second, to address the uncertainty in dynamic environment, distributed and multicore-aware elastic real-time scheduling algorithms are being developed that can adaptively adjust the invocation interval of various control tasks for schedulability and stability. Moreover, criticality-aware scheduling algorithms are being developed that consider the variable importance of control tasks under different situations. Finally, energy-efficient multicore scheduling algorithms and battery management schemes for hybrid/electric smart vehicles are being investigated.
The design and development of the proposed scheduling framework has a direct economic and societal impact, which can reduce car accidents and thus save lives. Moreover, with the development of new curricula on cyber-physical systems, this project also provides abundant topics and learning opportunities for under-represented students.
