Adaptive Multiple-Attribute Scenario LoRA Merge for Robust Perception in Autonomous Driving

Perception models for autonomous driving are predominantly trained on clear, daytime data, leaving their performance under rare conditions-particularly in multiple-attribute (joint weather-lighting) conditions such as night × rainy or night × snowy-an open challenge. To address this, we propose a parameter-efficient fine-tuning (PEFT) framework that dynamically applies lightweight, scenario-specific Low-Rank Adaptation (LoRA) experts. At its core, our method features an adaptive pipeline that dynamically determines the LoRA experts to apply based on the encountered environmental conditions. We validate our framework on a unified semantic segmentation benchmark (MUSES, BDD100K, and Cityscapes) covering six scenarios (day/night × weather). Our approach improves the mIoU by up to 3.23 points over a strong baseline in single-attribute settings, and in data-scarce multiple-attribute cases, merged LoRA experts outperform the baseline expert by up to 5.99 points, demonstrating effective generalization across compounded conditions.