In foggy weather conditions, autonomous vehicles suffer reduced maneuverability and
performance due to the degradation in sensor performances. Knowing that object detection …

AVs are affected by reduced maneuverability and performance due to the degradation of
sensor performances in fog. Such degradation can cause significant object detection errors …

Despite significant progress in vision-based detection methods, the task of detecting traffic
objects in foggy weather remains challenging. The presence of fog reduces visibility, which …

In foggy weather scenarios, the scattering and absorption of light by water droplets and
particulate matter cause object features in images to become blurred or lost, presenting a …

Autonomous Vehicle (AV) technologies are faced with several challenges under adverse
weather conditions such as snow, fog, rain, sun glare, etc. Object detection under adverse …

With the rapid development of deep learning in recent years, the level of automatic driving
perception has also increased substantially. However, automatic driving perception under …

This study introduces an advanced algorithm for intelligent vehicle target detection in hazy
conditions, aiming to bolster the environmental perception capabilities of autonomous …

Most object detection models for autonomous driving may experience a significant drop in
performance when deployed in real-world applications, due to the well-known domain shift …

The images in foggy scenes exhibit poor contrast, reduced saturation, tonal shift and loss of
detail, resulting in low accuracy and poor real-time detection of autonomous vehicles, which …

In the context of complex foggy environments, the acquired images often suffer from low
visibility, high noise, and loss of detailed information. The direct application of general object …