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

Perception Entropy: A Metric for Multiple Sensors Configuration Evaluation and Design