  Recent advances in Deep Neural Networks (DNNs) and sensor technologies are
enabling autonomous driving systems (ADSs) with an ever-increasing level of
autonomy. However, assessing their dependability remains a critical concern.
State-of-the-art ADS testing approaches modify the controllable attributes of a
simulated driving environment until the ADS misbehaves. In such approaches,
environment instances in which the ADS is successful are discarded, despite the
possibility that they could contain hidden driving conditions in which the ADS
may misbehave.
  In this paper, we present GENBO (GENerator of BOundary state pairs), a novel
test generator for ADS testing. GENBO mutates the driving conditions of the ego
vehicle (position, velocity and orientation), collected in a failure-free
environment instance, and efficiently generates challenging driving conditions
at the behavior boundary (i.e., where the model starts to misbehave) in the
same environment instance. We use such boundary conditions to augment the
initial training dataset and retrain the DNN model under test. Our evaluation
results show that the retrained model has, on average, up to 3x higher success
rate on a separate set of evaluation tracks with respect to the original DNN
model.
