Seeing Roads Through Words: A Language-Guided Framework for RGB-T Driving Scene Segmentation
arXiv:2602.07343v2 Announce Type: replace-cross Abstract: Robust semantic segmentation of road scenes under adverse illumination, lighting, and shadow conditions remain a core challenge for autonomous driving applications. RGB-Thermal fusion is a standard approach, yet existing methods apply static fusion strategies uniformly across all conditions, allowing modality-specific noise to propagate throughout the network. Hence, we propose CLARITY that dynamically adapts its fusion strategy to the detected scene condition. Guided by vision-language model (VLM) priors, the network learns to modulate
The continuous drive for more robust autonomous systems under varying conditions, combined with advancements in vision-language models, makes this research timely.
This development improves perception capabilities for autonomous vehicles in challenging environments, directly impacting safety and reliability goals for the industry.
Autonomous driving systems can now dynamically adapt their sensor fusion strategies based on real-time scene conditions, leading to more resilient perception.
- · Autonomous vehicle developers
- · Sensor manufacturers
- · AI software companies
- · Logistics and transportation sectors
- · Companies relying on static sensor fusion methods
- · Traditional sensor providers without AI integration
Improved reliability and safety metrics for autonomous driving platforms in adverse conditions.
Accelerated deployment and adoption of L4/L5 autonomous systems in diverse geographical and weather environments.
Enhanced trust in autonomous systems, potentially leading to wider societal integration and regulatory framework adjustments.
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Read at arXiv cs.AI