DiffAttn: Diffusion-Based Drivers' Visual Attention Prediction with LLM-Enhanced Semantic Reasoning
arXiv:2603.28251v3 Announce Type: replace-cross Abstract: Drivers' visual attention provides critical cues for anticipating latent hazards and directly shapes decision-making and control maneuvers, where its absence can compromise traffic safety. To emulate drivers' perception patterns and advance visual attention prediction for intelligent vehicles, we propose DiffAttn, a diffusion-based framework that formulates this task as a conditional diffusion-denoising process, enabling more accurate modeling of drivers' attention. To capture both local and global scene features, we adopt Swin Transfor
This development appears now as AI research rapidly progresses, focusing on critical real-world applications like autonomous driving safety, driven by advancements in diffusion models and LLMs.
Improving drivers' visual attention prediction is crucial for enhancing autonomous vehicle safety and reliability, directly impacting the adoption and trust in intelligent transport systems.
The explicit incorporation of diffusion models and LLM-enhanced semantic reasoning offers a more robust and human-like prediction of driver attention, potentially leading to safer and more perceptive autonomous systems.
- · Autonomous vehicle developers
- · Automotive safety systems
- · AI research institutions
- · Insurance companies
- · Developers of less sophisticated attention prediction models
More accurate visual attention prediction will lead to safer and more responsive autonomous driving systems.
Increased safety and reliability could accelerate the public adoption and regulatory approval of autonomous vehicles.
Wider deployment of such systems could transform logistics, urban planning, and the automotive industry's workforce over the long term.
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Read at arXiv cs.AI