Using control synthesis to generate corner cases: A case study on autonomous driving

TL;DR

This paper introduces a control synthesis-based method for generating corner cases in autonomous driving by computing controlled invariant sets and using them to identify safety violations and falsify control designs.

Contribution

It presents a novel approach combining controlled invariant set computations and dual reachability games to generate corner cases and verify control safety in autonomous driving systems.

Findings

Effective in generating corner cases for various control strategies

Can verify safety of classical and modern autonomous driving controllers

Applicable to real-world autonomous driving software

Abstract

This paper employs correct-by-construction control synthesis, in particular controlled invariant set computations, for falsification. Our hypothesis is that if it is possible to compute a "large enough" controlled invariant set either for the actual system model or some simplification of the system model, interesting corner cases for other control designs can be generated by sampling initial conditions from the boundary of this controlled invariant set. Moreover, if falsifying trajectories for a given control design can be found through such sampling, then the controlled invariant set can be used as a supervisor to ensure safe operation of the control design under consideration. In addition to interesting initial conditions, which are mostly related to safety violations in transients, we use solutions from a dual game, a reachability game for the safety specification, to find falsifying inputs. We also propose optimization-based heuristics for input generation for cases when the state is outside the winning set of the dual game. To demonstrate the proposed ideas, we consider case studies from basic autonomous driving functionality, in particular, adaptive cruise control and lane keeping. We show how the proposed technique can be used to find interesting falsifying trajectories for classical control designs like proportional controllers, proportional integral controllers and model predictive controllers, as well as an open source real-world autonomous driving package.