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.

本研究探讨了各种减少眩光的技术，包括针对自动驾驶车辆感知层所采用的计算机视觉任务的饱和像素感知眩光减少技术，通过基于各种性能指标评估表明该方法提高了计算机视觉的性能，对不同程度的眩光表现出出色的韧性。