Linnhoff Clemens, Hofrichter Kristof, Elster Lukas, Rosenberger Philipp, Winner Hermann
Institute of Automotive Engineering, Technical University of Darmstadt, 64289 Darmstadt, Germany.
Sensors (Basel). 2022 Jul 14;22(14):5266. doi: 10.3390/s22145266.
Safety validation of automated driving functions is a major challenge that is partly tackled by means of simulation-based testing. The virtual validation approach always entails the modeling of automotive perception sensors and their environment. In the real world, these sensors are exposed to adverse influences by environmental conditions such as rain, fog, snow, etc. Therefore, such influences need to be reflected in the simulation models. In this publication, a novel data set is introduced and analyzed. This data set contains lidar data with synchronized reference measurements of weather conditions from a stationary long-term experiment. Recorded weather conditions comprise fog, rain, snow, and direct sunlight. The data are analyzed by pairing lidar values, such as the number of detections in the atmosphere, with weather parameters such as rain rate in mm/h. This results in expectation values, which can directly be utilized for stochastic modeling or model calibration and validation. The results show vast differences in the number of atmospheric detections, range distribution, and attenuation between the different sensors of the data set.
自动驾驶功能的安全验证是一项重大挑战，部分通过基于仿真的测试来解决。虚拟验证方法总是需要对汽车感知传感器及其环境进行建模。在现实世界中，这些传感器会受到雨、雾、雪等环境条件的不利影响。因此，此类影响需要在仿真模型中体现出来。在本出版物中，引入并分析了一个新的数据集。该数据集包含来自一个固定长期实验的激光雷达数据以及同步的天气条件参考测量值。记录的天气条件包括雾、雨、雪和直射阳光。通过将激光雷达值（如大气中的检测数量）与天气参数（如毫米/小时的降雨率）配对来分析数据。这产生了期望值，可直接用于随机建模或模型校准与验证。结果表明，数据集中不同传感器在大气检测数量、距离分布和衰减方面存在巨大差异。
