Until now, the "cyber" component of automobiles has consisted of control algorithms and associated software for vehicular subsystems designed to achieve one or more performance, efficiency, reliability, comfort, or safety (PERCS) goals, primarily based on short-term intrinsic vehicle sensor data. However, there exist many extrinsic factors that can affect the degree to which these goals can be achieved.
Shared hardware resources like caches and memory introduce timing unpredictability for real-time systems. Worst-case execution time (WCET) analysis with shared hardware resources is often so pessimistic that the extra processing capacity of multicore systems is negated. We propose techniques to improve performance and schedulability for multicore systems.
The project is developing novel architectures for control and diagnosis of complex cyber-physical systems subject to stringent performance requirements in terms of safety, resilience, and adaptivity. These ever-increasing demands necessitate the use of formal model-based approaches to synthesize provably-correct feedback controllers.
