  Sampling critical testing scenarios is an essential step in intelligence
testing for Automated Vehicles (AVs). However, due to the lack of prior
knowledge on the distribution of critical scenarios in sampling space, we can
hardly efficiently find the critical scenarios or accurately evaluate the
intelligence of AVs. To solve this problem, we formulate the testing as a
continuous optimization process which iteratively generates potential critical
scenarios and meanwhile evaluates these scenarios. A bi-level loop is proposed
for such life-long learning and testing. In the outer loop, we iteratively
learn space knowledge by evaluating AV in the already sampled scenarios and
then sample new scenarios based on the retained knowledge. Outer loop stops
when all generated samples cover the whole space. While to maximize the
coverage of the space in each outer loop, we set an inner loop which receives
newly generated samples in outer loop and outputs the updated positions of
these samples. We assume that points in a small sphere-like subspace can be
covered (or represented) by the point in the center of this sphere. Therefore,
we can apply a multi-rounds heuristic strategy to move and pack these spheres
in space to find the best covering solution. The simulation results show that
faster and more accurate evaluation of AVs can be achieved with more critical
scenarios.
