## 3 An overview of AVs
### Challenges for AVs in Adverse Weather
#### 3.5.5 V2V, V2I, and VLC Communications  
The influence of meteorological conditions on network latency can sometimes prevent vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications from connecting in a direct line of sight. For safety applications, minimal latency is essential. While radio-based communications are less impacted by snow, they are nevertheless susceptible  
Figure 6: Weather impacts on AVs. to problems like solar flares and thunderstorms. Communication interruptions can result from bad weather such as fog, heavy rain, snow, and hail obstructing the visible light communication (VLC) line of sight. The process of OD in AVs unfolds in several key steps, beginning with the collection of image data by the sensor system. Following this, a Region of Interest (ROI) or regional proposal strategy is employed, marking a critical phase in object tracking. This approach is commonly utilized in devices such as cameras and stereoscopic systems, where ROIs serve as the foundational elements for tracking objects. The subsequent step involves the use of classifiers to meticulously analyze and refine shapes, edges, and lines. This analysis aids in the classification of targets and their identification based on human categories, embedding these ROIs within the image for object recognition. Various techniques, including sliding windows, Locally De-correlated Channel Features (LDCF), and constrained searches, are applied to locate ROIs efficiently. The extraction of features from these ROIs represents the third stage of the process. At this juncture, either DL-based methods or traditional manual OD algorithms can be employed for the extraction and classification of features. While manual feature extraction techniques can be advantageous for models that rely on lower-level features by facilitating manual ROI suggestions [52], their efficacy diminishes when dealing with complex features, rendering them less reliable. However, to derive useful and relevant data from video or photographs using a computing device, a great deal of computation has to be completed [53]. In contrast, DL methodologies enable systems to autonomously learn feature representations, thereby enhancing the accuracy and adaptability of feature extraction in dynamic and complex driving environments. The evolution of research on OD in bad weather depends heavily on datasets. They act as the building blocks for creating, refining, and testing algorithms and models. In this part, we are going to discuss a few of the datasets that are frequently utilized in this field of study. One of the most often used benchmarks for autonomous driving research is the KITTI [54] dataset. It features a range of various metropolitan settings. The collection offers LiDAR data, excellent-quality pictures, and annotations for cars. A2D2 [55] is a sizable dataset and delivers extensive annotations for several items, including vehicles, together with high-resolution camera and LiDAR data. Waymo's dataset [56], which is renowned for its comprehensive and varied gathering of data from AVs and includes information from a range of weather and lighting circumstances, is a useful tool for research on inclement weather. Among the noteworthy datasets, NuScenes [57], BDD100K [58], ApolloScape [59], and SYNTHIA [60] datasets offer diverse and extensive resources for exploring vehicle detection in challenging conditions. A quick overview of the weather conditions considered and the particular sensors utilized for gathering data for each dataset can be found in Table 2. The University of Michigan used a Segway robot to collect four seasons of LIDAR data for the NCLT [61] on campus. The first AV dataset, called the Canadian Adverse Driving Conditions (CADC) [62] dataset, focuses on snow conditions. The Challenging Conditions Dataset with Correspondences (ACDC) [54], created for training and testing lexical techniques for segmentation in difficult visual conditions, was later introduced by the same team.