  Autonomous Cyber-Physical Systems must often operate under uncertainties like
sensor degradation and shifts in the operating conditions, which increases its
operational risk. Dynamic Assurance of these systems requires designing runtime
safety components like Out-of-Distribution detectors and risk estimators, which
require labeled data from different operating modes of the system that belong
to scenes with adverse operating conditions, sensors, and actuator faults.
Collecting real-world data of these scenes can be expensive and sometimes not
feasible. So, scenario description languages with samplers like random and grid
search are available to generate synthetic data from simulators, replicating
these real-world scenes. However, we point out three limitations in using these
conventional samplers. First, they are passive samplers, which do not use the
feedback of previous results in the sampling process. Second, the variables to
be sampled may have constraints that are often not included. Third, they do not
balance the tradeoff between exploration and exploitation, which we hypothesize
is necessary for better search space coverage. We present a scene generation
approach with two samplers called Random Neighborhood Search (RNS) and Guided
Bayesian Optimization (GBO), which extend the conventional random search and
Bayesian Optimization search to include the limitations. Also, to facilitate
the samplers, we use a risk-based metric that evaluates how risky the scene was
for the system. We demonstrate our approach using an Autonomous Vehicle example
in CARLA simulation. To evaluate our samplers, we compared them against the
baselines of random search, grid search, and Halton sequence search. Our
samplers of RNS and GBO sampled a higher percentage of high-risk scenes of 83%
and 92%, compared to 56%, 66% and 71% of the grid, random and Halton samplers,
respectively.
