Hybrid-Flow Drive: Unifying Masked Diffusion and Continuous Embedding Flow for Autonomous Driving

End-to-end autonomous driving with vision-language-action (VLA) models requires trajectories that are accurate, consistent with the observed scene and navigation command, and reliable in long-tail cases. Diffusion language models provide an efficient alternative to purely autoregressive generation, but standard discrete masked-diffusion decoding is not well matched to planning: a fixed [MASK] token carries little information about the current noise level, supervision is often limited to corrupted positions, and standard decoding repeatedly discards partial evidence at unresolved tokens. We present Hybrid-Flow Drive, a hybrid flow diffusion VLA that replaces static masks with continuous, noise-aware token embeddings. Our approach has three main innovations: (1) Hybrid Flow Language Model (HFLM), which corrupts response embeddings in continuous space and gives each unresolved position an evolving state; (2) Next-Token-Invisible Bidirectional Attention (NBA), which prevents ground-truth leakage under continuous embeddings and allows dense supervision over response tokens; and (3) Verified Flow Decoding (VFD), which keeps the continuous state across denoising steps and checks the drafted sequence with a causal branch to recover autoregressive consistency. On the Waymo Open Dataset for End-to-End Driving, Hybrid-Flow Drive achieves a highly competitive Rater Feedback Score of 7.772, surpassing prior trajectory-supervised baselines. On nuScenes, it achieves an average L2 trajectory error of 0.31 m. Our results demonstrate that continuous noise-aware states can improve the human-aligned planning quality of diffusion-style end-to-end autonomous driving.