Title: DRYVR:Data-driven verification and compositional reasoning for automotive systems,
Abstract: We present the DRYVR framework for verifying hybrid control systems that are
described by a combination of a black-box simulator for trajectories and a
white-box transition graph specifying mode switches. The framework includes (a)
a probabilistic algorithm for learning sensitivity of the continuous
trajectories from simulation data, (b) a bounded reachability analysis
algorithm that uses the learned sensitivity, and (c) reasoning techniques based
on simulation relations and sequential composition, that enable verification of
complex systems under long switching sequences, from the reachability analysis
of a simpler system under shorter sequences. We demonstrate the utility of the
framework by verifying a suite of automotive benchmarks that include powertrain
control, automatic transmission, and several autonomous and ADAS features like
automatic emergency braking, lane-merge, and auto-passing controllers.