Vision-Language Assistant for Emotional Reactions to Risky Driving
This study introduces a vision-language pipeline that detects risky driving behaviors and generates emotionally expressive responses to support driver awareness and comfort. Although vision-language models have advanced perception and reasoning in autonomous driving, existing systems rarely consider the emotional dimension or real-world user experience. Keep Yelling Assistant (KYA) detects high-risk driving maneuvers in real time, such as sudden cut-ins. It then produces emotional responses through a large language model tailored to driver preferences. The framework comprises two core modules.
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%VioletVision-3B →
“Fuzzy title match (0.73): “Vision-Language Assistant for Emotional Reactions to Risky D” ≈ “VioletVision-3B””
- FuzzySimilar title/name (fuzzy) · 84%pytorch/vision →
“Fuzzy title match (0.92): “Vision-Language Assistant for Emotional Reactions to Risky D” ≈ “pytorch/vision””
- FuzzyOverlapping authors or contributors · 62%ultralytics/yolov5 →
“Shared author/contributor keys: choi”
- FuzzyOverlapping authors or contributors · 62%browser-use/browser-use →
“Shared author/contributor keys: lee”
- LinkedLinked via arxiv author · 85%Harine Choi →
“Vision-Language Assistant for Emotional Reactions to Risky Driving”
- LinkedLinked via arxiv author · 85%Eun Hak Lee →
“Vision-Language Assistant for Emotional Reactions to Risky Driving”
- LinkedLinked via arxiv author · 85%Zhengzhong Tu →
“Vision-Language Assistant for Emotional Reactions to Risky Driving”