A Novel Information-Theoretic Metric for Evaluating LiDAR Setups of Autonomous Vehicles

The sensor configuration of an autonomous vehicle (AV) is determined in the early development phase when specific perception algorithms are not yet available. Therefore, approaches based on synthetic raw data are necessary to evaluate different configurations. One sensor type used in AV is LiDAR, but developers should carefully consider the amount and placement of the sensors due to their high costs. In this contribution, we propose the Omni-Lidar Evaluation Score (OLES), a novel metric to evaluate different LiDAR configurations based on their simulated raw data. Our OLES metric combines information-theoretic quantities with coverage-based metrics, considering both the spatial coverage and the uniformity of a LiDAR point cloud distribution. We show the need for a new metric and provide details on implementing OLES using the open-source simulator CARLA. We demonstrate the effectiveness of our new metric in a simulation study and highlight its usefulness in the early phases of vehicle development. This research provides a means to evaluate the quality of LiDAR configurations and provides a basis for further optimizing sensor setups for AVs.