DryVR: Data-Driven Verification and Compositional Reasoning for Automotive Systems

  • Chuchu Fan
  • Bolun Qi
  • Sayan Mitra
  • Mahesh Viswanathan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10426)

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.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Chuchu Fan
    • 1
  • Bolun Qi
    • 1
  • Sayan Mitra
    • 1
  • Mahesh Viswanathan
    • 1
  1. 1.University of Illinois at Urbana-ChampaignChampaignUSA

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