DryVR: Data-driven verification and compositional reasoning for automotive systems

TitleDryVR: Data-driven verification and compositional reasoning for automotive systems
Publication TypeConference Paper
Year of Publication2017
AuthorsFan C, Qi B, Mitra S, Viswanathan M
Conference NameComputer Aided Verification - 29th International Conference, (CAV)
PublisherSpringer, Cham
Conference LocationHeidelberg, Germany
ISBN Number9783319633862

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.