Noise-Aware Deep Learning for Distinguishing Human and Autonomous Driving Behaviors in V2X Environments

Accurately identifying whether a vehicle is controlled by a human driver or by an autonomous policy is critical in future mixed-traffic environments for safety monitoring and liability attribution. To achieve accurate classification or identification, this paper proposes NMTT-Net (Noise-agnostic Multimodal Temporal Transformer Network), a multimodal time-series classifier that combines VAE-derived latent embeddings from front-view camera images with 18-dimensional vehicle dynamics signals. To maintain reliability under adverse weather and sensor corruption, NMTT-Net applies modality-specific noise detection and denoising prior to temporal encoding, and learns discriminative behavioral patterns using dual-path Temporal Transformer encoders with fixed-weight fusion. We construct a Human-to-Autonomy (H2A) dataset in the CARLA simulator under Town07 containing Human, PPO, and DDQN trajectories under clean and noisy conditions, in which image noise is induced by adverse weather and dynamics noise is modeled as Gaussian corruption. Experimental results show that NMTT-Net achieves 99.509% and 98.617% accuracy under clean and noisy conditions, respectively, resulting in a robustness gap (clean-to-noisy accuracy drop) of 0.892%, which is smaller than that of denoised single-modality baselines. These simulation-bound results demonstrate that noise-aware multimodal temporal fusion can enable reliable H2A identification under severe environmental degradation, although further study is necessary for real-world readiness.