融合情绪与路况，智能提醒驾驶者保持安全
Multimodal Drivers' Emotion Recognition and Safety-Oriented Intervention for Intelligent Transportation Systems
- 结合语音情绪和视觉路况，生成分步干预指令
- 实验表明框架能平衡风险提示与情绪安抚
- 适合智能交通、车载系统研发人员参考
驾驶员情绪会影响复杂路况下的风险感知、决策与车辆控制。现有研究多聚焦情绪识别，缺乏对结合情绪与道路感知的上下文敏感干预关注。本文提出一种以安全优先的多模态驾驶辅助框架，通过分析语音情绪线索与视觉道路状况，生成结构化驾驶干预。框架先提供道路安全提醒，再输出契合情绪的言语支持。我们构建了多模态数据集，将情感语音信号与结构化道路环境描述对齐，并引入CARE（上下文感知道路-情绪评估）分数，联合评估情绪识别、风险识别与干预生成效果。实验结果表明，该框架在环境风险通报与情绪感知调节之间取得良好平衡，为智能交通系统提供了可行的安全驱动方向。
原文摘要 · Abstract (English)
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.
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