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• A new multimodal framework is introduced that fuses images and inertial measurements for road surface classification.method 95%
• The framework uses a lightweight bidirectional cross-attention module and an adaptive gating layer to adjust modality contributions.technical 90%
• A new dataset, ROAD, is introduced, comprising real-world multimodal recordings, a large vision-only subset, and a synthetic subset.method 95%
• The method achieves a +1.4 pp improvement over the previous state-of-the-art on the PVS benchmark.result 90%
• The method achieves an +11.6 pp improvement on the multimodal ROAD subset.result 90%
• The framework demonstrates stable performance across challenging visual conditions, including nighttime, heavy rain, and mixed-surface transitions.result 85%
• The approach offers a scalable, robust foundation for road surface understanding, particularly relevant for regions with environmental variability and cost constraints.market 80%
• The method's performance may depend on high-quality sensor data.limitation 70%

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