Abstract
Vision sensors are versatile and can capture a wide range of visual cues, such as color, texture, shape, and depth. This versatility, along with the relatively inexpensive availability of machine vision cameras, played an important role in adopting vision-based environment perception systems in autonomous vehicles (AVs). However, vision-based perception systems can be easily affected by glare in the presence of a bright source of light, such as the sun or the headlights of the oncoming vehicle at night or simply by light reflecting off snow or ice-covered surfaces; scenarios encountered frequently during driving. In this paper, we investigate various glare reduction techniques, including the proposed saturated pixel-aware glare reduction technique for improved performance of the computer vision (CV) tasks employed by the perception layer of AVs. We evaluate these glare reduction methods based on various performance metrics of the CV algorithms used by the perception layer. Specifically, we considered object detection, object recognition, object tracking, depth estimation, and lane detection which are crucial for autonomous driving. The experimental findings validate the efficacy of the proposed glare reduction approach, showcasing enhanced performance across diverse perception tasks and remarkable resilience against varying levels of glare.
Abstract (translated)
视觉传感器具有多功能，可以捕捉各种视觉线索，如颜色、纹理、形状和深度。这种多功能，再加上机器视觉摄像头相对较低的价格，在自动驾驶车辆（AVs）中采用基于视觉的环境感知系统发挥了重要作用。然而，基于视觉的感知系统很容易受到在明亮光源下存在的眩光的影响，例如太阳或夜间或仅仅是由于雪或冰表面反射的光线；这些情况在驾驶过程中经常遇到。在本文中，我们研究了各种眩光减除技术，包括为提高AV感知层计算机视觉（CV）任务的性能而提出的饱和像素感知眩光减除技术。我们根据CV算法使用感知层所实现的各种性能指标评估这些眩光减除方法。具体来说，我们考虑了物体检测、物体识别、物体跟踪、深度估计和车道检测，这些对于自动驾驶至关重要。实验结果证实了所提出的眩光减除方法的有效性，展示了在各种感知任务中出色的性能和对于眩光水平变化的非凡的鲁棒性。
URL
https://arxiv.org/abs/2404.10992
