冻结ACC策略的自适应安全过滤：基于共形残差校准
Adaptive Safety Filtering for Frozen ACC Policies via Conformal Residual Calibration
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中文总结 AI 辅助
针对冻结ACC策略部署时违反约束的问题，提出残差感知共形动作过滤（RACF），通过校准残差分位数生成运行裕度，在2,400个试验中提升安全性19.9个百分点并降低投影频率，同时减少计算开销。
中文摘要 AI 辅助
冻结的自适应巡航控制（ACC）策略在部署动力学与训练条件不同时可能违反约束。我们提出残差感知共形动作过滤（RACF），该方法校准固定名义预测器的残差，并将其分位数转换为有限模型动作投影的运行裕度。完成的转移会更新裕度和候选选择，而无需重新训练策略。在包含2,400个控制器试验单元的注册比较中，自适应RACF实现了94.3%的情节安全性，相比评估的名义CBF-QP基线提高了19.9个百分点，同时将投影频率从8.11%降低至6.63%。一项对照研究分离出残差裕度注入带来的4.54个百分点的改进。在另一项匹配的硬件评估中，自适应方法相对于鲁棒CBF-QP将平均摊销滚动时间减少了21.2%，安全情节为161/180对比170/180。我们刻画了将一步残差覆盖与约束满足联系起来条件，并量化了观察到的安全-计算权衡。
英文摘要
Frozen adaptive cruise control (ACC) policies can violate constraints when deployment dynamics differ from their training conditions. We propose residual-aware conformal action filtering (RACF), which calibrates residuals of a fixed nominal predictor and converts their quantile into an operating margin for finite-model action projection. Completed transitions update margins and candidate selection without retraining the policy. In a registered comparison over 2,400 controller-trial units, Adaptive RACF achieves 94.3% episode safety, improving by 19.9 percentage points over the evaluated nominal CBF-QP baseline while reducing projection frequency from 8.11% to 6.63%. A controlled study isolates a 4.54-point improvement from residual-margin injection. In a separate matched-hardware evaluation, Adaptive reduces mean amortized rollout time by 21.2% relative to Robust CBF-QP, with 161/180 versus 170/180 safe episodes. We characterize conditions linking one-step residual coverage to constraint satisfaction and quantify the observed safety-computation trade-offs.
发表机构
- Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
- Wuhan University of Technology（武汉理工大学）
- Shanghai Jiao Tong University（上海交通大学）
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