LiDAR Sensor Modeling for Autonomous Driving Using a Dual-Driven Method

In this article, we propose a dual-driven modeling method for vehicle-mounted LiDAR sensors. The method has a two-stage modeling framework comprising point cloud position modeling and intensity modeling, respectively. In the spatial position modeling stage, we generate ideal LiDAR point clouds by employing the geometric relationships of spatial rays. Subsequently, we integrate the LiDAR detection equation with a probability model to simulate the attenuation and noise. In the intensity modeling stage, a network InPNet is leveraged to train an intensity model. This model is able to predict accurate reflection intensity for point clouds generated in the previous stage. Experiments have shown that the proposed method is able to simulate point clouds within ±10% errors on various driving scenarios. We also mix the simulated point clouds as augmentation data with the real scanning point clouds and employ them to train several representative 3-D object detection models under clear and adverse weather conditions. The results demonstrate promising prospects for engineering applications.