# A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
## 4 Road Surface Classification
### Discussions
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Work**} & \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Value**} & \multicolumn{2}{c}{**Dataset**} \\ \cline{5-6}  & & & & **Images** & **Classes** & **Countries** \\ \hline
[66] & VGG16 & Acc & 0.91 & 5 k & 5: bare, partly snow covered (<25\%, 25-50\%, 50-75\%, fully snow covered) & \\
[67] & SqueezeNet & Acc & 0.97 & 5.3 k & 4: dry, wet, slush, snow/ice & Public domain \\
[69] & ResNet50 & Acc & 0.92 & 18.5 k & 6: asphalt, dirt, grass, wet asphalt, cobblestone, & UK, Germany \\
[73] & Own CNN with & Acc & 0.95 & 10 k & 5: dry, wet, snowy, muddy roads, others & UK, Germany \\  & Gait-ReLU & & & & \\
[73] & RCNet & Acc & 0.99 & 20 k & 5: dry, curvy, ice, rough, wet & UK, Germany \\
[65] & Dual-stream CNN & F1 & 0.94 & 4.3 k & 4: snowy, icy, wet, slushy & Canada \\
[76] & Vision Transformers & Acc & 0.92 & 2.5 k & 7: sunny, cloudy, foggy, rainy, wet, clear, snowy & UK \\
[78] & Baseline (AlexNet & Acc & 0.91 & 14 k & 3: bare pavement, partial snow cover, full snow cover & Canada \\
[84] & ResNet18 & Acc & 0.97 & 15 k & 3: clear, light snow, heavy snow & USA \\
[84] & ResNet18 & Acc & 0.99 & 15 k & 3: dry, snowy, wet/slushy & USA \\
[85] & ResNet50 & Acc & 0.98 & 12.5 k & 3: dry, wet, icy & China, Australia \\
[86] & Deep ensemble & Acc & 0.95 & 510 k & 4: flat road, sinusoidal road, manhole and pothole, & South Korea \\  & with LSTM & & & & bump & \\
[87] & Custom CNN & Acc & 0.85 & 9.2 k & 4: asphalt, concrete, cobblestone, dirt roads & Argentina \\
[88] & CNN-SP & Acc & 0.97 & 9.8 k & 20: normal road segments, road anomalies, obstacles & Italy \\
[89] & Custom CNN & Acc & 0.93 & 9 datasets & 3: dirt, cobblestone, asphalt & Brazil \\
[90] & Custom CNN & Acc & 0.92 & 11 k & 2: asphalt, snow & South Korea \\
[92] & Custom CNN & Acc & 0.95 & 62 k & 3: asphalt, paved, unpaved & Brazil \\
[92] & Custom CNN & Acc & 0.94 & 5 k & 3: asphalt, paved, unpaved & Germany \\
[92] & Custom CNN & Acc & 0.99 & 1.4 k & 3: asphalt, paved, unpaved & Brazil \\
[94] & U-NET with & Acc & 0.97 & 70 k & 11: asphalt, paved, unpaved, markings, speed-bump, cats-eye, storm-drain, patch, water-puddle, & Japan, UK, Italy \\  & ResNet50 & & & & \\
[96] & AlexNet & Acc & 0.85 & 0.5 k & 9: asphalt, concrete, trylinka, concrete paver blocks, & Poland \\  & & & & & \\In the domain of RSC, the diversity of CNN architectures is evident, with a noticeable preference for models such as ResNet, VGG16, and custom CNN designs, alongside novel approaches like Vision Transformers and attention mechanisms. ResNet, particularly ResNet50 and ResNet18, stands out for its deep residual learning framework, enabling the training of networks that are much deeper than previously possible, thereby improving accuracy. Its use across different datasets underscores its robustness and adaptability to varying road surface conditions. VGG16, known for its simplicity and depth, offers an advantage in extracting rich feature representations, making it suitable for classifying complex road surface images. Custom CNNs tailored to specific datasets indicate a trend toward optimizing network structures to achieve higher performance, demonstrating the field's move towards specialization. Vision Transformers, a more recent development, introduces an architecture that departs from traditional convolutional approaches, focusing instead on self-attention mechanisms, which can capture global dependencies within an image, offering promising results in classifying road surfaces under diverse conditions. The variation in RSC datasets reflects the need to capture a wide range of surface types and conditions, including seasonal changes, weather effects, and material differences, to develop models that are truly adaptable and robust across global road infrastructures.