Range-measuring sensors play a critical role in autonomous driving systems. While LiDAR technology has been dominant, its vulnerability to adverse weather conditions is well-documented. This paper focuses on secondary adverse conditions and the implications of ill-reflective surfaces on range measurement sensors. We assess the influence of this condition on the three primary ranging modalities used in autonomous mobile robotics: LiDAR, RADAR, and Depth-Camera. Based on accurate experimental evaluation the papers findings reveal that under ill-reflectivity, LiDAR ranging performance drops significantly to 33% of its nominal operating conditions, whereas RADAR and Depth-Cameras maintain up to 100% of their nominal distance ranging capabilities. Additionally, we demonstrate on a 1:10 scaled autonomous racecar how ill-reflectivity adversely impacts downstream robotics tasks, highlighting the necessity for robust range sensing in autonomous driving.

翻译：距离测量传感器在自动驾驶系统中扮演着关键角色。尽管激光雷达技术一直占据主导地位，但其在恶劣天气条件下的脆弱性已有充分记录。本文聚焦于次生不利条件及低反射表面对距离测量传感器的影响。我们评估了该条件对自主移动机器人领域三种主要测距模态——激光雷达、雷达与深度相机——的作用。基于精确实验评估，研究结果显示：在低反射条件下，激光雷达的测距性能显著下降至其标称工作条件的33%，而雷达与深度相机可保持高达100%的标称距离测量能力。此外，我们通过1:10比例的自主赛车模型实验，展示了低反射条件如何对下游机器人任务产生不利影响，凸显了自动驾驶中鲁棒距离感知的必要性。