# A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
## 2 Review Methodology  
This study's narrative review was predominantly conducted using the Web of Science (WoS) database, complemented by the Google Scholar database, centered on journal and conference articles published between 2018 and the beginning of 2024. Figure 1 presents a bibliometric analysis regarding the number of publications reviewed in this work in accordance with the publication year. Search criteria based on various keywords influenced this bibliometric analysis. In the case of RDD, these keywords were mainly road damage detection, road crack detection, and asphalt damage. In the same idea, for RSC, the main search keywords were road surface classification, road surface detection, road surface monitoring, and road classification. Figures 2 and 3 present a keyword analysis resulting from the analyzed papers for RDD and RSC, respectively. In each network, a node represents a keyword, with the node size reflecting how frequently the keyword appears. Links between nodes indicate keyword co-occurrence; thicker links represent higher co-occurrence frequency. Colors differentiate thematic clusters, grouping together multiple related keywords. Figure 4 presents a pie chart that provides a clear visual comparison of publications categorized into conference papers and journal articles, highlighting their status with respect to WOS indexing. It illustrates that a higher proportion of journal articles (33 out of 39) are indexed by WoS compared to conference papers, where only 28 out of 46 are WoS indexed. This discrepancy may suggest that journal articles tend to meet Wos's indexing criteria more consistently than conference papers, potentially reflecting higher standards or more rigorous peer review processes. The reasoning behind centering our investigation on DL methods, particularly Convolutional Neural Networks (CNNs), stems from the significant strides these technologies have made in the automotive area. CNNs, owing to their advanced feature extraction abilities, are remarkably effective for tasks that require detailed visual data interpretation, such as identifying various forms of road damage and categorizing road surface types. These networks could handle the diverse and unpredictable nature of road environments, including changes in texture, lighting, and weather conditions. Figure 1: Bibliometric analysis: number of publications per year. Figure 2: Keyword analysis for Road Damage Detection (RDD) papers considered. Traditional methods often struggle with such complexity and the sheer volume of data, whereas CNNs have repeatedly set new performance standards in similar image-based applications. This trend underscores their superiority and dependability compared to older techniques. Our survey, therefore, delves into how DL, with a focus on CNNs, is reshaping road infrastructure analysis, offering insights into the current State Of The Art (SOTA) and suggesting directions for future research in this crucial field. The articles for the first part of the survey, RDD, were categorized into two distinct groups based on the methods employed: one-stage detectors or two-stage detectors. This classification was undertaken to provide a structured and comprehensive overview of the field, reflecting the diverse approaches and technologies currently in use. Regarding RDD, one-stage detectors, known for their speed and efficiency in real-time applications, represent a crucial segment of research, particularly appealing for scenarios demanding rapid processing. In contrast, two-stage detectors, with their heightened accuracy and detailed object assessment, are pivotal in applications where P is paramount. The second part of the survey is dedicated to RSC, where the articles are divided into two primary categories: the nature of a road's surface material and the nature of the  
Figure 4: Distribution of Web of Science (WoS) Indexed and Non-WoS Indexed Conference Papers. Figure 3: Keyword analysis for Road Surface Classification (RSC) papers considered. environmental conditions that have an impact on a surface.