  Sensor configuration, including the sensor selections and their installation
locations, serves a crucial role in autonomous driving. A well-designed sensor
configuration significantly improves the performance upper bound of the
perception system. However, as leveraging multiple sensors is becoming the
mainstream setting, existing methods mainly focusing on single-sensor
configuration problems are hardly utilized in practice. To tackle these issues,
we propose a novel method based on conditional entropy in Bayesian theory to
evaluate the sensor configurations containing both cameras and LiDARs.
Correspondingly, an evaluation metric, perception entropy, is introduced to
measure the difference between two configurations, which considers both the
perception algorithm performance and the selections of the sensors. To the best
of our knowledge, this is the first method to tackle the multi-sensor
configuration problem for autonomous vehicles. The simulation results,
extensive comparisons, and analysis all demonstrate the superior performance of
our proposed approach.
