# DRYVR:Data-driven verification and compositional reasoning for automotive systems

## TL;DR

DRYVR is a framework that combines data-driven sensitivity analysis, probabilistic reachability, and compositional reasoning to verify complex hybrid automotive systems efficiently.

## Contribution

It introduces a novel combination of simulation-based sensitivity learning and compositional verification techniques for hybrid control systems.

## Key findings

- Successfully verified automotive benchmarks including powertrain and autonomous features.
- Demonstrated effectiveness in handling long switching sequences in hybrid systems.
- Provided scalable verification methods for complex automotive control 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.