Realistic Rainy Weather Simulation for LiDARs in CARLA Simulator (2312.12772v1)

Abstract: Employing data augmentation methods to enhance perception performance in adverse weather has attracted considerable attention recently. Most of the LiDAR augmentation methods post-process the existing dataset by physics-based models or machine-learning methods. However, due to the limited environmental annotations and the fixed vehicle trajectories in the existing dataset, it is challenging to edit the scene and expand the diversity of traffic flow and scenario. To this end, we propose a simulator-based physical modeling approach to augment LiDAR data in rainy weather in order to improve the perception performance of LiDAR in this scenario. We complete the modeling task of the rainy weather in the CARLA simulator and establish a pipeline for LiDAR data collection. In particular, we pay special attention to the spray and splash rolled up by the wheels of surrounding vehicles in rain and complete the simulation of this special scenario through the Spray Emitter method we developed. In addition, we examine the influence of different weather conditions on the intensity of the LiDAR echo, develop a prediction network for the intensity of the LiDAR echo, and complete the simulation of 4-feat LiDAR point cloud data. In the experiment, we observe that the model augmented by the synthetic data improves the object detection task's performance in the rainy sequence of the Waymo Open Dataset. Both the code and the dataset will be made publicly available at https://github.com/PJLab-ADG/PCSim#rainypcsim.

• The paper presents a Spray Emitter method that replicates tire-driven water splashes under varied speeds and water depths to realistically simulate rain effects on LiDAR data.
• It introduces an intensity prediction network utilizing multi-channel inputs to estimate rain-affected LiDAR echo intensities, significantly enhancing object detection accuracy.
• The creation of a 10,000-frame synthetic dataset validates improved performance of 3D object detection models in adverse rainy conditions, bridging simulation and real-world gaps.

Realistic Rainy Weather Simulation for LiDARs in CARLA Simulator

The paper "Realistic Rainy Weather Simulation for LiDARs in CARLA Simulator" introduces a novel approach to enhancing LiDAR data perception in adverse weather conditions through realistic simulation. This work has significant implications for autonomous driving technologies, as it addresses the challenges faced by perception algorithms under rainy conditions, which inherently add noise and decrease the reliability of LiDAR systems.

Overview

The paper proposes a method to simulate rainy weather in the CARLA simulator, focusing specifically on how rain affects LiDAR data. Existing methods primarily rely on post-processing of clear-condition datasets using physics-based models or machine learning techniques. However, these are limited by fixed trajectories and environmental annotations, rendering them less effective in diversifying traffic scenarios or accurately simulating dynamic weather phenomena.

Key Contributions

1. Spray Emitter Method: The authors introduce a Spray Emitter method to simulate the spray and splash effects caused by vehicle tires on wet roads, a crucial addition that replicates the real-world challenges that LiDAR systems face in rain. This is based on physical modeling of tire-spray mechanisms, considering the vehicle speed, water depth, and droplet dynamics.
2. Intensity Prediction Network: They develop a prediction network to estimate the intensity of LiDAR echoes, which is affected by weather conditions. This network uses a multi-channel data input comprising RGB information, semantic labels, depth, and weather data to predict intensity variations accurately, integrating insights on physical interactions during rain.
3. Dataset Generation: A new synthetic dataset is created comprising 10,000 frames of simulated LiDAR data under various rainfall conditions. This dataset aims to improve 3D object detection performance in rainy weather, offering greater realism and diversity compared to traditional datasets.

Experimental Validation

The experiments conducted demonstrate enhanced object detection performance when models are trained using the synthetic rainy dataset. Notably, the augmented training data leads to improvements in test scenarios involving real rainy conditions, confirming the value of such simulated data for robust perception in autonomous vehicles. The Pointpillars baseline recorded a notable increase in both BEV and 3D object detection metrics when the simulated data was integrated into the training pool, suggesting that the synthetic data effectively bridges the domain gap between simulation and real-world applications.

Implications and Future Directions

The implications of this research are profound for the development of robust autonomous driving systems capable of operating in varied weather conditions. Simulating realistic adverse weather impacts on LiDAR data can significantly enhance the robustness of perception algorithms, potentially reducing the need for extensive real-world data collection under diverse conditions.

Future research could explore extending this simulation methodology to other weather scenarios like fog and snow, further improving the generalizability of perception algorithms. Moreover, integrating these simulations with other sensor data, such as camera and radar systems, might offer more comprehensive solutions for multi-modal sensor fusion methods in autonomous systems.

In summary, this paper provides an essential advancement in simulating real-world weather impacts on LiDAR systems, presenting a valuable tool for researchers and developers focused on autonomous driving technologies.