# A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
## 4 Road Surface Classification
### The Nature of Environmental Conditions  
Environmental conditions have a significant impact on road surfaces, influencing their integrity and performance. Factors such as rain, snow, or ice could lead to either deterioration (cracking or potholing) or safe driving. Recognizing these environmental influences enables the development of more resilient road surfaces and the implementation of timely maintenance strategies, ensuring safer and more reliable roadways in order to develop autonomous driving. As we review the SOTA in this domain, we will highlight innovative approaches and methodologies that have been developed to accurately identify and classify road surface types, underscoring the significance of environmental conditions. For example, in [66] the authors utilize a pre-trained VGG16 [17] for RSC on a dataset collected from two highway sections in South-Western Ontario, Canada. The dataset was analyzed under three granularity levels: two-class (Bare, With Snow/Ice), three-class (Bare, Partly Snow Covered, Fully Snow Covered), and five-class descriptions, to offer a nuanced understanding of winter road conditions. The five-class description provides the most detailed classification, distinguishing between various degrees of snow coverage. The model achieved the highest classification accuracy with the two-class description, suggesting that while detailed classifications provide valuable insights into road conditions, simpler classification schemes may benefit from higher accuracy and robustness in practical applications. A different approach is presented in [67], where the authors implemented a comprehensive two-stage approach to classify RSC and road friction estimate (RFE) using front-view camera images from vehicles. In the first stage, they explored various CNN architectures, including SqueezeNet [68] and traditional CNN models with different depths, to identify region-specific features. They focused on features from drivable surface textures, the sky, and surrounding areas, achieving high classification \(Acc\). In the second stage, a rule-based model segmented the ego-lane drivable surface into patches, classifying them to assess RFE. This innovative method allowed for the estimation of friction levels across different road conditions by evaluating the patchiness of the road surface. The dataset for this study was derived from publicly available sources, consisting of 3750 training images and 1550 test images, with four classes: dry, wet, slush, and snow/ice. Similarly, the authors from [69] curated a mixed dataset from public datasets (KITI [70], Oxford Robocar Dataset [71]), and images sourced from the web to train two CNN models: ResNet50 and InceptionV3 [72]. These models aimed to distinguish between six types of road surfaces: asphalt, dirt, grass, wet asphalt, cobblestone, and snow. The best result achieved was a 92% average classification accuracy, provided by ResNet50. Figure 9: Example of Road Surface Classification (RSC) detection from [65]. An innovation at the neural level is the work from [73], where the authors present a modified activation function based on the Rectified Linear Units (ReLU) [74]. This adjustment, named Gai-ReLU, aims to improve classification accuracy by addressing the issue of data leakage for negative inputs. Their CNN architecture, optimized for this task, demonstrated superior performance with a classification _Acc_ of 94.89% on 10,000 images from the Oxford RobotCar dataset and KITTI, with 5 labels: dry, wet, snowy, muddy roads, and others (road surface conditions that do not specifically fall into the primary defined categories). In the same manner, in [75], the authors propose RCNet, a CNN-based model which classifies the road into five categories: curvy, dry, ice, rough, and wet. The RCNet architecture, although based on CNNs, is optimized for extracting significant features from road images under varying conditions, such as illumination and weather changes.