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

by   Glen Chou, et al.

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.


page 1

page 2

page 3

page 4


Runtime Safety Assurance for Learning-enabled Control of Autonomous Driving Vehicles

Providing safety guarantees for Autonomous Vehicle (AV) systems with mac...

MPC-based Imitation Learning for Safe and Human-like Autonomous Driving

To ensure user acceptance of autonomous vehicles (AVs), control systems ...

Runtime Interchange for Adaptive Re-use of Intelligent Cyber-Physical System Controllers

Cyber-Physical Systems (CPSs) such as those found within autonomous vehi...

A New Simulation Metric to Determine Safe Environments and Controllers for Systems with Unknown Dynamics

We consider the problem of extracting safe environments and controllers ...

Safe and efficient collision avoidance control for autonomous vehicles

We study a novel principle for safe and efficient collision avoidance th...

Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning

We apply Deep Q-network (DQN) with the consideration of safety during th...

Towards Guaranteed Safety Assurance of Automated Driving Systems with Scenario Sampling: An Invariant Set Perspective (Extended Version)

How many scenarios are sufficient to validate the safe Operational Desig...

Please sign up or login with your details

Forgot password? Click here to reset