For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle’s environment through sensors. Perception is challenging, wherein it suffers from dynamic objects and continuous environmental changes. The issue grows worse due to interrupting the quality of perception via adverse weather such as snow, rain, fog, night light, sand storms, strong daylight, etc. In this work, we have tried to improve camera-based perception accuracy, such as autonomous-driving-related object detection in adverse weather. We proposed the improvement of YOLOv8-based object detection in adverse weather through transfer learning using merged data from various harsh weather datasets. Two prosperous open-source datasets (ACDC and DAWN) and their merged dataset were used to detect primary objects on the road in harsh weather. A set of training weights was collected from training on the individual datasets, their merged versions, and several subsets of those datasets according to their characteristics. A comparison between the training weights also occurred by evaluating the detection performance on the datasets mentioned earlier and their subsets. The evaluation revealed that using custom datasets for training significantly improved the detection performance compared to the YOLOv8 base weights. Furthermore, using more images through the feature-related data merging technique steadily increased the object detection performance.

Research on autonomous vehicles is making significant advances in recent years. To operate an autonomous vehicle safely and effectively, precise localization is essential. This study aims to present state of the art in localization to scientists new to the area. It presents and summarizes works from the field of localization, and suggests a classification for the works. Approaches to localization are mainly divided into three categories: conventional localization, machine-learning-based localization, and vehicle-to-everything (V2X) localization. Conventional localization primarily depends on high-definition (HD) maps or on certain marks, such as landmarks and road marks. Machine-learning-based localization approaches include using neural networks, end-to-end approaches, as well as reinforcement learning for performing, or improving, localization. And lastly, V2X localization methods localize a vehicle by communicating with other vehicles (V2V) or infrastructures (V2I). This study not only presents a bigger picture of the area of localization in autonomous driving, but also presents the potentials and drawbacks of different localization methods. At the end of the review, some research areas that are open for future research are also highlighted.

For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle’s environment through sensors. Perception is a challenging task suffering from dynamic objects and continuous environmental changes. The issue gets worse due to interrupting the quality of perception by adverse weather like snow, rain, fog, night light, sand storm, strong daylight, etc. In this work, we have tried to improve camera-based perception accuracy, such as autonomous driving-related object detection in adverse weather. We proposed the improvement of YOLOv8-based object detection in adverse weather through transfer learning using merged data from various harsh weather datasets. Two prosperous open-source datasets (ACDC and DAWN) and their merged dataset were used to detect primary objects on the road in harsh weather. A set of training weights were collected from training on the individual datasets, their merged version, and several subsets of those datasets according to their characteristics. A comparison between the training weights also occurred by evaluating the detection performance on the above-mentioned datasets and their subsets. The evaluation revealed that using custom datasets for training significantly improves the detection performance compared to the YOLOv8 base weights. And using more images through the feature-related data merging technique steadily increases the object detection performance.