用物理约束生成更真实的恶劣天气激光雷达数据，提升自动驾驶感知能力。
Simulating Realistic LiDAR Data Under Adverse Weather for Autonomous Vehicles: A Physics-Informed Learning Approach
- 结合物理规律建模信号衰减与几何畸变，构建可解释的生成框架。
- 在雪/雨真实数据集上，生成数据与实测强度分布差异极小（MSE、KL等指标显著降低）。
- 训练模型性能接近真实数据，适合用于恶劣天气下自动驾驶系统测试与训练。
准确的激光雷达（LiDAR）仿真对自动驾驶至关重要，尤其在恶劣天气条件下。现有方法难以捕捉激光信号与大气现象之间的复杂交互，导致仿真结果不真实。本文提出一种物理信息学习框架（PICWGAN），通过将信号衰减的物理约束与几何一致的退化模型融入物理信息学习流程，有效缩小了仿真与现实之间的差距。在真实数据集CADC（雪）和Boreas（雨）以及VoxelScape上的评估表明，该方法生成的数据能精准复现真实强度模式。定量指标（包括MSE、SSIM、KL散度和Wasserstein距离）显示其强度分布具有统计一致性。此外，在该框架增强的数据上训练的3D目标检测模型，性能优于基线，达到与使用真实数据训练相当的水平。结果验证了该方法在提升激光雷达数据真实性及支持恶劣天气下鲁棒感知方面的有效性。
原文摘要 · Abstract (English)
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.
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