This research aims to assess the functionality of the VLP-32 LiDAR sensor, which serves as the principal sensor for object recognition in autonomous vehicles. The evaluation is conducted by simulating edge conditions the sensor might encounter in a controlled darkroom setting. Parameters for environmental conditions under examination encompass measurement distances ranging from 10 to 30 m, varying rainfall intensities (0, 20, 30, 40 mm/h), and different observation angles (0°, 30°, 60°). For the material aspects, the investigation incorporates reference materials, traffic signs, and road surfaces. Employing this diverse set of conditions, the study quantitatively assesses two critical performance metrics of LiDAR: intensity and NPC (number of point clouds). The results indicate a general decline in intensity as the measurement distance, rainfall intensity, and observation angles increase. Instances were identified where the sensor failed to record intensity for materials with low reflective properties. Concerning NPC, both the effective measurement area and recorded values demonstrated a decreasing trend with enlarging measurement distance and angles of observation. However, NPC metrics remained stable despite fluctuations in rainfall intensity.
Keywords: automotive LiDAR; darkroom; intensity; point cloud; rainfall.