用优化乘子构建新警报信号，显著提升导航安全监测漏检率
Dual Stress: Runtime Safety Monitoring for Safety-Constrained MPC Navigation
- 利用模型预测控制的对偶乘子生成应力信号，补充传统几何预警
- 应力信号发现85起几何方法遗漏的碰撞，是几何方法发现数的4.7倍
- 适合高阶自动驾驶系统中的实时安全监控场景
自主导航的运行时危险监测通常依赖几何量：预测间距、碰撞时间、所需减速度。基于模型预测控制的安全约束求解器在每次控制步骤中，会额外计算出一个被现有监测忽略的信息通道——其约束优化问题的Karush-Kuhn-Tucker乘子，该乘子反映为维持安全而付出的边际控制努力。本文评估了这些乘子的时域加权和（即对偶应力信号）是否可作为与已有几何警告互补的危险监测手段。在物理仿真器中，针对预注册的交叉场景进行测试，将该应力信号与15个几何检测器（调参至相同误报预算）进行对比。结果显示，应力信号发现的未被几何电池覆盖的碰撞事件是几何电池反向发现数的4.7倍（85比18）；两者结合可预警75%的可行制动碰撞，远超几何电池单独使用的不足50%。
原文摘要 · Abstract (English)
Runtime hazard monitors for autonomous naviga- tion are conventionally built from geometric quantities: predicted clearance, time to collision, and required deceleration. A model-predictive controller that enforces safety through explicit con- straints computes, as a by-product of every control step, a second information channel that such monitors ignore: the Karush-Kuhn-Tucker multipliers of its constrained optimization, which measure the marginal control effort spent to maintain safety against each obstacle. This paper evaluates whether a horizon-weighted sum of those multipliers, a dual stress signal, provides a hazard monitor complementary to the geometric warnings the same state already supports. We compare it against a battery of fifteen geometric detectors tuned to a matched false-alarm budget, on preregistered held-out crossing scenarios driven through a physics simulator. The stress alarm actionably flags 4.7 times as many collisions missed by the entire geometric battery as the geometric battery flags in return (85 versus 18); combined, the two channels warn of three quarters of the collisions for which braking remained feasible, against under half for the geometric battery alone.
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