Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

Abstract

In mobile systems, fog, rain, snow, haze, and sun glare are natural phenomena that can be very dangerous for drivers. In addition to the visibility problem, the driver must face also the choice of speed while driving. The main effects of fog are a decrease in contrast and a fade of color. Rain and snow cause also high perturbation for the driver while glare caused by the sun or by other traffic participants can be very dangerous even for a short period. In the field of autonomous vehicles, visibility is of the utmost importance. To solve this problem, different researchers have approached and offered varied solutions and methods. It is useful to focus on what has been presented in the scientific literature over the past ten years relative to these concerns. This synthesis and technological evolution in the field of sensors, in the field of communications, in data processing, can be the basis of new possibilities for approaching the problems. This paper summarizes the methods and systems found and considered relevant, which estimate or even improve visibility in adverse weather conditions. Searching in the scientific literature, in the last few years, for the preoccupations of the researchers for avoiding the problems of the mobile systems caused by the environmental factors, we found that the fog phenomenon is the most dangerous. Our focus is on the fog phenomenon, and here, we present published research about methods based on image processing, optical power measurement, systems of sensors, etc.

RoBétArmé Project: Human-robot collaborative construction system for shotcrete digitization and automation through advanced perception, cognition, mobility and additive manufacturing skills

“…To allow robots to work in turbid conditions, the first line of recourse is to use longer wavelengths such as radar or thermal imaging, but their lower spatial resolution is unfortunately often prohibitive. The mitigation of the effects of turbidity with near-visible–light sensors is carried out by first detecting then filtering its effects 43 . Alleviation is typically conducted through image filtering 44 or object filtering 45 , sensor fusion, and application-specific solutions that exploit specificities of a given task to improve the information content of sensor data 46 .…”

Deep Camera-Radar Fusion with Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions

“…In this work, we used the CARLA [81] simulator to create a simulated environment for autonomous driving from which we collected camera and radar data. Seven (7) different types of common road participants were included in our datasets. Since the camera observations and radar detections were associated, the radar detections were sparsely overlapped in white dots on the camera image, as shown in Figure 8.…”

“…AVs encounter several difficulties under adverse weather conditions, such as snow, fog, haze, shadow, and rain [1][2][3][4][5][6][7][8]. AVs may suffer from poor decision-making and control if their perception systems are degraded by adverse weather.…”

Fog Detection through Image Processing Methods

“…By examining the nuances and efficacy of these image processing approaches, this review aims to provide a comprehensive understanding and potential directions for advancing fog detection algorithms within the domain of image processing. A review of the solutions related to visibility enhancement and fog detection has been presented in [5].…”