Classifying Automated Driving Systems Using Telematics Data: A Comparative Study of GRU, LSTM, and Transformer Models

The rapid advancement of sensing, artificial intelligence, and in-vehicle computing has accelerated deployment of automated driving systems (ADSs). As vehicles move toward software-defined architectures, multiple driving stacks—including manufacturer and aftermarket solutions—may coexist, raising the need to verify which system is actively controlling the vehicle from observable behavior alone. This paper investigates ADS identification using only harmonized vehicle telematics, without proprietary controller states or perception sensors. We compared three deep sequence models—Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and a Transformer Encoder—for distinguishing Comma Openpilot, Tesla Autopilot, Cadillac Super Cruise, and manual driving from eleven shared features over 32-timestep windows. On clean held-out data, all three architectures achieved strong performance (macro-F1 above 0.91; mean ± std over 20 random seeds), with the Transformer Encoder attaining the highest mean macro-F1 and the GRU providing competitive, lower-cost alternatives. We further evaluated robustness under a structured telematics noise framework covering six atomic threat families and four compound multi-fault conditions at different severity levels using a two-arm protocol over 7 random seeds: clean training (Arm A) versus evaluation matched noise-augmented training (Arm B). Under clean training, degradation was highly threat-dependent: correlated cross-feature noise and stochastic drift—and their compounds—caused the largest absolute and relative drops in macro-F1, whereas delay-with-hold temporal jitter and temporal delay had comparatively minor effects. This pattern is shared across GRU, LSTM, and Transformer Encoder models. Evaluation matched augmentation preserved clean-test accuracy while substantially reducing degradation, including at held-out severities and on compound faults withheld from training. The results show that telemetry-based ADS identification is feasible at scale and that reliable deployment requires explicit attention to continuous and cross-feature impairments.