# A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
## 4 Road Surface Classification
### The Nature of Environmental Conditions
The dataset used for training and validation is the Oxford RobotCar dataset, supplemented with YouTube video frames for the curvy road category, totaling over 20,000 images. Another innovation and the neural level is presented in [65], where the authors use a dual-stream CNN model that integrates both visible light and thermal images. The dual-stream CNN architecture consists of two independent input streams, each with its convolutional block fed by one type of image (either visible light or thermal). The CNN model itself is adapted from VGG16 but restructured into a simpler seven-layer configuration to increase processing speed while maintaining performance. An \(F1\) score of 0.94 was computed by training the model on 4244 pairs of images acquired from various roads in Montreal, Canada, during winter and divided into four distinct classes: snowy, icy, wet, and slushy. An innovative approach is presented in [76], where the authors introduce the usage of Vision Transformers (ViTs) models [77]. Unlike traditional CNNs, ViTs apply attention mechanisms to dynamically assign weights to pixels, focusing the model on relevant image features. Another innovation of the paper consists of creating a dataset of 2498 images from online videos from the United Kingdom from which seven classes are derived from weather conditions: sunny, cloudy, foggy, rainy, wet, clear, and snowy. A comparison between multiple CNNs is made in [78], where the authors evaluated seven SOTA models such as InceptionV3, Inception-Resnet-v2 [79], Xception [80], DenseNet169, MobileNetV2 [81], and NASNet [82], all of which were pre-trained on the ImageNet dataset. Additionally, they proposed a baseline model inspired by AlexNet [83] and VGG [17], which was significantly less complex in terms of layers and parameters. The core contribution was their experiment in simplifying the baseline model through an ablation study, successfully reducing its complexity without substantially affecting performance. The models were trained on a dataset consisting of 14,000 labeled images collected from 40 stations connected to the Ontario Road Weather Information System (RWIS) during the 2017-2018 Winter season. Another work based on multiple comparisons is presented in [84], where the authors compared AlexNet, GoogleNet [11], and ResNet18 adapted via transfer learning to identify road surfaces based on three weather conditions (clear, light snow, and heavy snow) and three surface conditions (dry, snowy, wet/slushy). The dataset includes 30,000 images (15,000 images from road surface conditions) obtained from roadside webcams from Wyoming, USA, with the best results for the ResNet18 model, reaching 97% _Acc_ for weather detection and 99% for RSC. In [85] the authors compare both classical ML techniques (Support Vector Machine--SVM, K-Nearest Neighbor--KNN, K Means Clustering--KMC) and advanced DL approaches (ResNet50 and MobileNet) to classify three road conditions: dry, wet, and icy. The best results were obtained using DL techniques by training the models on 12,545 images from various roads in China and Australia.