  Autonomous vehicle manufacturers recognize that LiDAR provides accurate 3D
views and precise distance measures under highly uncertain driving conditions.
Its practical implementation, however, remains costly. This paper investigates
the optimal LiDAR configuration problem to achieve utility maximization. We use
the perception area and non-detectable subspace to construct the design
procedure as solving a min-max optimization problem and propose a bio-inspired
measure -- volume to surface area ratio (VSR) -- as an easy-to-evaluate cost
function representing the notion of the size of the non-detectable subspaces of
a given configuration. We then adopt a cuboid-based approach to show that the
proposed VSR-based measure is a well-suited proxy for object detection rate. It
is found that the Artificial Bee Colony evolutionary algorithm yields a
tractable cost function computation. Our experiments highlight the
effectiveness of our proposed VSR measure in terms of cost-effectiveness
configuration as well as providing insightful analyses that can improve the
design of AV systems.
