Diffusion-SAFE: Diffusion-Native Human-to-Robot Driving Handover for Shared Autonomy
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912949650587648 |
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| author | Fan, Yunxin Kennedy III, Monroe |
| author_facet | Fan, Yunxin Kennedy III, Monroe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09889 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diffusion-SAFE: Diffusion-Native Human-to-Robot Driving Handover for Shared Autonomy Fan, Yunxin Kennedy III, Monroe Robotics I.2.9; H.5.2 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. |
| title | Diffusion-SAFE: Diffusion-Native Human-to-Robot Driving Handover for Shared Autonomy |
| topic | Robotics I.2.9; H.5.2 |
| url | https://arxiv.org/abs/2505.09889 |