发表机构
Budapest University of Technology and Economics; Tongji University（布达佩斯技术与经济大学; 同济大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对智能交通系统，提出安全优先的多模态驾驶员辅助框架，构建多模态数据集并引入CARE分数，实现环境风险报告与情绪感知调节的平衡，提供安全驱动的可行方向。
AI 中文摘要
驾驶员情绪会影响复杂路况下的风险感知、决策及车辆控制。现有研究主要聚焦于驾驶员情绪识别，而对同时考虑驾驶员情绪与道路感知的情境感知干预关注有限。本文提出一种安全优先的多模态驾驶员辅助框架，该框架分析语音衍生的情绪线索与视觉道路状况，以生成结构化驾驶干预措施。该框架首先提供道路安全提醒，随后生成与情绪匹配的口头支持。我们通过将情绪语音信号与结构化道路环境描述符对齐构建了一个多模态数据集，并引入CARE（情境感知道路-情绪评估）分数，以联合评估情绪识别、风险识别及干预生成。实验结果表明，所提框架在环境风险报告与情绪感知口头调节之间实现了平衡，为智能交通系统提供了可行的安全驱动方向。
英文摘要
Driver emotions can affect risk perception, decision-making, and vehicle control under complex road conditions. Existing studies mainly focus on driver emotion recognition, while limited attention has been given to context-aware intervention that jointly considers driver emotion and road perception. This paper proposes a safety-prioritized multimodal driver assistance framework that analyzes speech-derived emotional cues and visual road conditions to generate structured driving interventions. The framework first provides road safety reminders and then generates emotion-aligned verbal support. We construct a multimodal dataset by aligning emotional speech signals with structured road environment descriptors and introduce the CARE (Context-Aware Road-Emotion Evaluation) score to jointly evaluate emotion recognition, risk identification, and intervention generation. Experimental results show that the proposed framework balances environmental risk reporting and emotion-aware verbal regulation, providing a feasible safety-driven direction for intelligent transportation systems.
Comments6 pages, 2 figures, IEEE ITSC 2026
