Sage Deer让车载助手能懂人、会共情，还能主动引导驾驶决策。
Sage Deer: A Super-Aligned Driving Generalist Is Your Copilot
- 通过多模态感知与个性化偏好建模，实现对用户情绪和行为的精准理解。
- 在自建基准上，对用户生理与情感状态的识别准确率达92.3%。
- 适合自动驾驶交互、智能座舱研发人员参考，推动人车协同进化。
智能驾驶座舱作为智能驾驶的关键组成部分，需满足不同用户的舒适性、交互性与安全性需求。本文提出超对齐且通用的驾驶代理SAGE DeeR。Sage Deer具备三大优势：(1) 超对齐：根据用户偏好与认知偏差做出差异化响应；(2) 通用性：可融合多视角、多模态输入，推理用户生理指标、面部情绪、手部动作、身体姿态、驾驶场景及行为决策；(3) 自激发：能在语言空间中激发隐式思维链，进一步增强通用性与超对齐能力。此外，我们构建了多个数据集并建立了大规模基准测试，用于评估该系统在感知-决策链路中的表现以及超对齐精度。
原文摘要 · Abstract (English)
The intelligent driving cockpit, an important part of intelligent driving, needs to match different users' comfort, interaction, and safety needs. This paper aims to build a Super-Aligned and GEneralist DRiving agent, SAGE DeeR. Sage Deer achieves three highlights: (1) Super alignment: It achieves different reactions according to different people's preferences and biases. (2) Generalist: It can understand the multi-view and multi-mode inputs to reason the user's physiological indicators, facial emotions, hand movements, body movements, driving scenarios, and behavioral decisions. (3) Self-Eliciting: It can elicit implicit thought chains in the language space to further increase generalist and super-aligned abilities. Besides, we collected multiple data sets and built a large-scale benchmark. This benchmark measures the deer's perceptual decision-making ability and the super alignment's accuracy.
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