Hevisov David, Liemert André, Reitzle Dominik, Kienle Alwin
Institute for Laser Technologies in Medicine and Metrology at the University of Ulm (ILM), D-89081 Ulm, Germany.
Sensors (Basel). 2024 Aug 7;24(16):5121. doi: 10.3390/s24165121.
In the context of autonomous driving, the augmentation of existing data through simulations provides an elegant solution to the challenge of capturing the full range of adverse weather conditions in training datasets. However, existing physics-based augmentation models typically rely on single scattering approximations to predict light propagation under unfavorable conditions, such as fog. This can prevent the reproduction of important signal characteristics encountered in a real-world environment. Consequently, in this work, Monte Carlo simulations are employed to assess the relevance of multiple-scattered light to the detected LiDAR signal in different types of fog, with scattering phase functions calculated from Mie theory considering real particle size distributions. Bidirectional path tracing is used within the self-developed GPU-accelerated Monte Carlo software to compensate for the unfavorable photon statistics associated with the limited detection aperture of the LiDAR geometry. To validate the Monte Carlo software, an analytical solution of the radiative transfer equation for the time-resolved radiance in terms of scattering orders is derived, thereby providing an explicit representation of the double-scattered contributions. The results of the simulations demonstrate that the shape of the detected signal can be significantly impacted by multiple-scattered light, depending on LiDAR geometry and visibility. In particular, double-scattered light can dominate the overall signal at low visibilities. This indicates that considering higher scattering orders is essential for improving AI-based perception models.
在自动驾驶的背景下，通过模拟对现有数据进行增强，为在训练数据集中捕捉各种不利天气条件这一挑战提供了一个巧妙的解决方案。然而，现有的基于物理的增强模型通常依赖于单散射近似来预测在不利条件下（如雾中）的光传播。这可能会妨碍再现现实世界环境中遇到的重要信号特征。因此，在这项工作中，采用蒙特卡罗模拟来评估不同类型雾中多次散射光与检测到的激光雷达信号的相关性，其中散射相位函数是根据考虑实际粒径分布的米氏理论计算得出的。在自主开发的GPU加速蒙特卡罗软件中使用双向路径追踪，以补偿与激光雷达几何结构有限检测孔径相关的不利光子统计。为了验证蒙特卡罗软件，推导了辐射传输方程关于散射阶数的时间分辨辐射率的解析解，从而明确表示了双散射贡献。模拟结果表明，检测到的信号形状会受到多次散射光的显著影响，这取决于激光雷达几何结构和能见度。特别是，在低能见度下，双散射光可能主导整体信号。这表明考虑更高的散射阶数对于改进基于人工智能的感知模型至关重要。
