Simulating Realistic LiDAR Data Under Adverse Weather for Autonomous Vehicles: A Physics-Informed Learning Approach

TL;DR

This paper introduces a physics-informed learning framework called PICWGAN to generate realistic LiDAR data under adverse weather, improving simulation fidelity and perception robustness for autonomous vehicles.

Contribution

The paper presents a novel physics-informed learning approach that models atmospheric effects on LiDAR signals, reducing the sim-to-real gap in adverse weather conditions.

Findings

PICWGAN closely mimics real-world intensity patterns in adverse weather datasets.

Models trained on PICWGAN-augmented data outperform baselines in 3D object detection.

Quantitative metrics show statistically consistent intensity distributions with real data.

Abstract

Accurate LiDAR simulation is crucial for autonomous driving, especially under adverse weather conditions. Existing methods struggle to capture the complex interactions between LiDAR signals and atmospheric phenomena, leading to unrealistic representations. This paper presents a physics-informed learning framework (PICWGAN) for generating realistic LiDAR data under adverse weather conditions. By integrating physicsdriven constraints for modeling signal attenuation and geometryconsistent degradations into a physics-informed learning pipeline, the proposed method reduces the sim-to-real gap. Evaluations on real-world datasets (CADC for snow, Boreas for rain) and the VoxelScape dataset show that our approach closely mimics realworld intensity patterns. Quantitative metrics, including MSE, SSIM, KL divergence, and Wasserstein distance, demonstrate statistically consistent intensity distributions. Additionally, models trained on data enhanced by our framework outperform baselines in downstream 3D object detection, achieving performance comparable to models trained on real-world data. These results highlight the effectiveness of the proposed approach in improving the realism of LiDAR data and enabling robust perception under adverse weather conditions.