Modeling and Verifying Intelligent Automotive Cyber-Physical Systems

Exhaustive state space exploration based verification of cyber-physical system designs remains a challenge despite five decades of active research into formal verification. On the other hand, models of intelligent automotive cyber-physical systems continue to grow in complexity. The testing of intelligent automotive models often uses human subjects, is expensive, and can not be performed unless the system has already been prototyped and is ready for human interaction. We propose the use of machine learning methods to learn stochastic models of human-vehicle interaction. Simulation based validation of even critical designs often uses randomized testing and is subject to financial budget considerations in practice. We argue that a combination of statistical and randomized verification approaches are suitable for verifying complex intelligent cyber-physical systems in an era of multi-core processors.