感知计算量的决策价值

中文摘要 AI 辅助

自适应感知会在预期感知能有所改善的输入上花费额外的计算量。当感知服务于下游决策系统时，更好的感知输出并不必然带来更好的决策。我们将感知计算量的决策价值定义为将输入从廉价感知模式升级到昂贵感知模式所导致的下游损失的改变。由于该价值可能为负，感知计算量的分配应参照受预算约束的决策预言机来评判，并将均匀的全保真推理作为基线而非上界。我们提出了DEEP（升级感知的决策评估）基准，该基准在选择性、延迟和能量预算下，根据该预言机对预升级分配器进行评分，并为每个分配器自身的计算量计费。利用部署在KITTI和nuScenes上的单目几何方法，我们发现34%至54%的能改变下游损失的升级反而使其恶化；对于已发表的PDM-Closed规划器，在nuPlan上使用真实检测器结果进行开环评估时，同样会出现有害升级。在nuScenes上，感知层面的增益与决策价值在符号上经常不一致。这种不匹配具有实际后果：根据漏检目标的感知增益而非决策价值在固定的可部署信号中进行选择，会使实际测试决策增益平均降低全廉价损失的7.4%。学习型分配器通过发现有益的升级恢复了预言机的一部分价值，但其选择的危害几乎与随机选择一样多，并且一旦在20%的延迟预算下对其自身计算量计费，只有约全检测器计算量3.5%的轻量级路由器仍能胜过随机选择。

英文摘要

Adaptive perception spends extra computation on inputs where perception is expected to improve. When perception feeds a downstream decision system, a better perception output need not produce a better decision. We define the decision value of perception compute as the change in downstream loss from escalating an input from a cheap to an expensive perception mode. Because this value can be negative, the allocation of perception compute should be judged against a budget-constrained decision oracle, with uniform full-fidelity inference as a baseline rather than an upper bound. We introduce DEEP (Decision Evaluation for Escalated Perception), a benchmark that scores pre-escalation allocators against this oracle under selection, latency and energy budgets, charging each allocator for its own computation. With deployed monocular geometry on KITTI and nuScenes, we find that 34--54% of the escalations that change downstream loss make it worse; harmful escalations also occur for the published PDM-Closed planner, evaluated open-loop on nuPlan with real detector outcomes. On nuScenes, perception-level gain frequently disagrees in sign with decision value. This mismatch has practical consequences: choosing among fixed deployable signals by missed-object perception gain rather than by decision value reduces realized test decision gain by 7.4% of the all-cheap loss on average. Learned allocators recover part of the oracle's value by finding beneficial escalations but select nearly as much harm as random, and once their own computation is charged at a 20% latency budget, only the lightweight routers, at about 3.5\% of a full detector pass, still beat random.

发表机构

• Hanoi University of Science and Technology (HUST)（河内科技大学）

机构由 AI 辅助整理，请以论文原文为准。