ECoLAD: Selecting Anomaly Detectors for Automotive Deployment via Compute-Reduction Evaluation
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 ...
| Authors | Kadir-Kaan Özer, René Ebeling, Markus Enzweiler |
|---|---|
| Primary category | cs.LG |
| Published | 11 March 2026 |
| Last updated at source | 12 July 2026 |
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