Scalable and Efficient Multi-Weather Classification for Autonomous Driving with Coresets, Pruning, and Resolution Scaling

Autonomous vehicles require robust perception systems capable of operating in diverse weather conditions, including snow, rain, fog, and storms. In this work, we present a scalable and efficient approach for multi-weather classification in autonomous driving, leveraging the WEDGE (WEather images by DALL-E GEneration) dataset. Our study investigates three complementary techniques to enhance classification performance and efficiency: Coreset Selection, Resolution Scaling, and Model Compression via Adaptive Pruning and Quantization. Specifically, we evaluate the impact of coreset selection methods (random and margin-based) at varying data fractions (e.g., 1, 0.75, 0.5, 0.25, 0.1), assess model robustness under low-resolution settings (224x224, 112x112, 56x56), and demonstrate that adaptive pruning combined with 8-bit quantization can reduce model size by up to 85\% while maintaining competitive classification accuracy. Experimental results validate the effectiveness of our integrated approach, providing a scalable and robust solution for multi-weather classification. This work advances the feasibility of deploying perception models in real-world autonomous driving systems operating under adverse weather conditions and limited computational resources.