为汽车异常检测设计可预测延迟的评估方法，避免高精度但不可用模型。
ECoLAD: Selecting Anomaly Detectors for Automotive Deployment via Compute-Reduction Evaluation
- 构建计算量递减阶梯评估框架，模拟车载有限算力
- 轻量级经典模型在真实场景中实现高评分速率且优于随机基线
- 适合需要低延迟部署的自动驾驶系统开发者参考
汽车异常检测模型常在工作站硬件上仅以准确率优劣进行筛选，但车载监控需在有限CPU并行能力下保证可预测的评分延迟。这种差异导致某些离线表现优异的模型实际无法部署。本文提出ECoLAD（面向异常检测的效率计算阶梯），一种面向汽车时序异常检测（TSAD）的部署导向评估协议。ECoLAD定义了单调递减的计算量阶梯，包含明确的CPU线程上限、仅整数的超参数缩放机制、推理与全量运行时间分离，以及可审计的运行日志。应用于自有车载遥测数据及两个公开基准，结果表明：准确率稳定性与部署可行性可能背离——部分深度模型虽保持AUC-PR，但吞吐量不可行；而轻量级经典模型在持续高评分速率的同时，相较随机基线有正向提升，提供了一套符合实际约束的检测器筛选流程。
原文摘要 · Abstract (English)
Automotive anomaly detectors are often selected from accuracy only benchmarks on workstation class hardware, whereas in-vehicle monitoring requires predictable scoring latency under limited CPU parallelism. This mismatch can make methods that appear competitive offline infeasible for deployment. We present ECoLAD (Efficiency Compute Ladder for Anomaly Detection), a deployment-oriented evaluation protocol for automotive time-series anomaly detection (TSAD). ECoLAD defines a monotone compute reduction ladder with explicit CPU thread caps, mechanical integer only hyperparameter scaling, inference/full run timing separation, and auditable run logs. Applied to proprietary in-vehicle telemetry and two public benchmarks, it shows that accuracy stability and deployment feasibility can diverge: some deep detectors retain AUC-PR while losing feasible throughput, whereas lightweight classical detectors sustain high scoring rates with positive lift above the random baseline, providing a practical screening procedure for detector selection under deployment relevant constraints.
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