用扩散模型实现人机共驾安全交接，平滑过渡且避免危险混合。
Diffusion-SAFE: Diffusion-Native Human-to-Robot Driving Handover for Shared Autonomy
- 基于双扩散模型：一个评估人类意图，一个引导安全路径生成。
- 仿真与实车测试成功率93.0%/87.0%，交接过程平滑无突变。
- 无需手工设计评分函数，可直接从演示数据学习行为与安全策略。
驾驶中的共驾需要预判人类行为、提前识别风险并安全平滑地移交控制权。本文提出Diffusion-SAFE，一个闭环框架，包含两个扩散模型：一个评估器用于预测多模态的人类意图动作序列以实现概率风险检测；一个安全引导的副驾模型通过地图基础的安全证书梯度，引导去噪过程向安全区域收敛。当检测到风险时，通过部分扩散完成控制转移：将人类计划向前加噪至中间层级，再由安全引导副驾进行去噪。前向扩散比例ρ作为连续接管旋钮——ρ越小，输出越贴近人类意图；ρ越大，控制权越倾向副驾，避免动作级融合带来的混合不安全问题。与依赖手工设计评分函数的方法不同，本方法的扩散形式可直接从示范数据中同时支持安全评估与计划生成。我们在仿真环境和基于ROS的真实赛车上进行了评估，手控交接成功率分别为93.0%（仿真）和87.0%（实车），实现了平滑过渡。
原文摘要 · Abstract (English)
Shared autonomy in driving requires anticipating human behavior, flagging risk before it becomes unavoidable, and transferring control safely and smoothly. We propose Diffusion-SAFE, a closed-loop framework built on two diffusion models: an evaluator that predicts multimodal human-intent action sequences for probabilistic risk detection, and a safety-guided copilot that steers its denoising process toward safe regions using the gradient of a map-based safety certificate. When risk is detected, control is transferred through partial diffusion: the human plan is forward-noised to an intermediate level and denoised by the safety-guided copilot. The forward-diffusion ratio $ρ$ acts as a continuous takeover knob-small $ρ$ keeps the output close to human intent, while increasing $ρ$ shifts authority toward the copilot, avoiding the mixed-unsafe pitfall of action-level blending. Unlike methods relying on hand-crafted score functions, our diffusion formulation supports both safety evaluation and plan generation directly from demonstrations. We evaluate Diffusion-SAFE in simulation and on a real ROS-based race car, achieving 93.0%/87.0% (sim/real) handover success rates with smooth transitions.
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