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February 21, 2026Sensors0 citationsOpen Access

Adaptive Multiple-Attribute Scenario LoRA Merge for Autonomous Driving Perception

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

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Authors

RKRyosuke KawataJLJoonho LeeYGYanlei Gu

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Overview

This framework enhances perception in autonomous driving across challenging environmental conditions, improving accuracy.

Key Points

  • The aim is to improve perception models for autonomous driving in complex environmental conditions.
  • Developed a parameter-efficient fine-tuning framework with dynamic LoRA experts.
  • Implemented an adaptive pipeline that selects LoRA experts based on environmental conditions.
  • Validated on semantic segmentation benchmarks such as MUSES, BDD100K, and Cityscapes.
  • Achieved up to 3.23 point improvement in mean Intersection over Union (mIoU) in single-attribute settings.
  • In multiple-attribute scenarios, merged LoRA experts outperformed the baseline by up to 5.99 points.
  • Demonstrated effective generalization capabilities in data-scarce conditions.

Cite This Study

Kawata et al. (2026) studied this question.

synapsesocial.com/papers/69994bef873532290d020125https://doi.org/10.3390/s26041336
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