Human-In-The-Loop Classification of Adaptive Cruise Control at a Freeway Scale

The goal of this paper is to estimate whether a human or Adaptive Cruise Control (ACC) is managing a vehicle's speed control, based on observations by external sensors. The driving characteristics of individual vehicles---whether human-driven or ACC-controlled---play a crucial role in shaping overall traffic flow. To enable advanced traffic control strategies tailored to specific vehicle behaviors, this paper introduces a time-series deep learning classifier that leverages multiple models, including One-Dimensional Convolutional Neural Networks (1D-CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Temporal Fusion Transformers (TFT). These models distinguish between human-driven and ACC-controlled trajectories using signals such as the ego vehicle's velocity, the distance to the leading vehicle, and derived features. Unlike previous studies relying solely on simulation data, our classifier uses large-scale, real-world datasets from field experiments and daily commute data. By utilizing low-latency, low-anomaly signals decoded from Controller Area Network (CAN) bus messages, the model achieves a high accuracy of 98.85% in classifying human-driven and ACC-controlled vehicles within three seconds, outperforming existing methods that require longer trajectory data or pre-calibrated models. The approach is scalable and can be integrated with large-scale traffic trajectory datasets, such as those from the I-24 Motion project, enabling more precise estimation of ACC penetration, fuel consumption, and emissions.