ECoLAD：通过计算降级评估选择车载异常检测器

Jul 16, 2026

Automotive anomaly detectors are often chosen by offline accuracy on workstation hardware, but in-vehicle monitoring demands predictable inference scoring latency under tight CPU parallelism limits. This mismatch can render apparently competitive models unusable after deployment. ECoLAD (Efficiency Compute Ladder for Anomaly Detection) introduces a deployment-oriented evaluation protocol for time-series anomaly detection, using a monotone compute-reduction ladder, thread caps, integer-only scaling, timing separation, and auditable logs. Applied to in-vehicle telemetry and benchmarks, it shows accuracy feasibility can diverge and enables practical detector screening.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages4

Reading Time10 min

Introduction

Industrial deployment workflow use

ECoLAD Protocol

Compute Reduction Ladder

Mechanical Hyperparameter Scaling

Throughput feasibility target sweep

ECoLAD: Selecting Anomaly Detectors for Automotive Deployment via Compute-Reduction Evaluation Kadir-Kaan Özer1 ,2 , René Ebeling 1 , Markus Enzweiler2 arXiv :2603 . 10926v2 [ cs .LG] 12 Jul 2026 Abstract—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. I. INTRODUCTION Modern vehicles continuously produce telemetry from powertrain controllers, chassis actuators, electronic control units (ECUs), and body systems. Detecting anomalies in these streams supports early fault discovery, predictive maintenance, and safety monitoring. Onboard deployment imposes hard constraints: inference latency must be predictable, CPU parallelism is often limited to a single thread, and memory bandwidth is restricted. Existing time-series anomaly detection (TSAD) benchmarks evaluate methods under unconstrained execution on workstation hardware and report accuracy as the sole criterion. This misrepresents deployment feasibility in two distinct ways. First, method rankings can change when compute budgets and CPU parallelism are jointly reduced. Second, a method that tops an accuracy leaderboard may become throughput infeasible on constrained hardware with no accuracy degradation at all, an effect entirely invisible inaccuracy only rankings. ECoLAD addresses both failure modes by specifying: (i) a monotone compute reduction ladder with four tiers that scale model capacity and thread count jointly; (ii) mechanically determined, integer only scaling rules applied uniformly across method families without per tier retuning; and (iii) a throughput target sweep reporting coverage and best achievable AUC-PR under each target. We contribute both the protocol (reusable on any hardware and dataset) and an 1Mercedes-Benz AG, Germany. {kadir .oezer, [rene.ebeling}@mercedes-benz.com](rene.ebeling}@mercedes-benz.com) 2Esslingen University of Applied Sciences, Germany. [markus.enzweiler@hs-esslingen.de](markus.enzweiler@hs-esslingen.de) % datasets covered 100 80 60 40 20 0 Feasibility/coverage vs target τ (inference throughput) 103 104 105 106 Throughput target τ (windows/s) Fig. 1: Coverage vs. throughput target τ on the CPU-1T tier (wps = N/tinf , Sec. II-C) . Each curve shows the fraction of evaluated entities whose inference throughput exceeds τ . The horizontal line marks the 50% feasibility reference. Method abbreviations: IForest: Isolation Forest; LOF: Local Outlier Factor; HBOS: Histogram-Based Outlier Score; COPOD: Copula-Based Outlier Detection; PCA: linear subspace baseline (see Table II) . empirical instantiation on automotive telemetry and public benchmarks. Prior TSAD evaluation works [1], [2], [3], [4], [5], [6] standardize datasets, metrics, and execution environments. ECoLAD differs in formalizing how model capacity and parallelism are reduced under deployment pressure, adding explicit laddering, thread caps, and throughput feasibility analysis as first-class protocol variables. a) Industr

FAQs

Why can accuracy-only TSAD benchmarks fail for automotive deployment?

Rankings can change when compute budgets and CPU parallelism are reduced, and a top accuracy method may become throughput-infeasible on constrained hardware without showing any accuracy degradation. Such failure is invisible when only inaccuracy rankings are considered.

What is ECoLAD’s compute reduction ladder?

ECoLAD defines a monotone compute reduction ladder with four tiers that jointly scale model capacity and CPU thread count. Each tier fixes an execution backend, enforces thread caps, applies a compute reduction factor, and logs all cap/config changes.

How does ECoLAD help select anomaly detectors before vehicle integration?

In an automotive development workflow, ECoLAD can act as a reproducible feasibility gate by discarding detectors that cannot sustain required scoring rates under CPU-thread limits, then ranking the remaining candidates by detection quality.

Jul 16, 2026

Upload by Lucas Martin