  This paper describes a verification case study on an autonomous racing car
with a neural network (NN) controller. Although several verification approaches
have been proposed over the last year, they have only been evaluated on
low-dimensional systems or systems with constrained environments. To explore
the limits of existing approaches, we present a challenging benchmark in which
the NN takes raw LiDAR measurements as input and outputs steering for the car.
We train a dozen NNs using two reinforcement learning algorithms and show that
the state of the art in verification can handle systems with around 40 LiDAR
rays, well short of a typical LiDAR scan with 1081 rays. Furthermore, we
perform real experiments to investigate the benefits and limitations of
verification with respect to the sim2real gap, i.e., the difference between a
system's modeled and real performance. We identify cases, similar to the
modeled environment, in which verification is strongly correlated with safe
behavior. Finally, we illustrate LiDAR fault patterns that can be used to
develop robust and safe reinforcement learning algorithms.

Systems and Control;Robotics;Systems and Control