Although human-driven vehicles (HVs) tend to follow autonomous vehicles (AVs) more stably, this stability comes at a cost: HVs must maintain continuous attention and perform frequent micro-adjustments that conflict with their expectations and habitual driving patterns. As a result, driver discomfort accumulates under seemingly smooth traffic conditions, which may paradoxically increase latent risk and contribute to rear-end collisions. We define this phenomenon as non-explicit coordination failure (NECF). Using the Waymo Open Dataset, this study systematically identifies and quantifies the NECF pattern induced by AV driving in no-lane-change, straight car-following scenarios. By constructing a lateral control framework, vehicles traveling in the same lane as the AV (AVLVs) are treated as the affected group, while vehicles one lane away from the AV (SLVs) serve as a weakly affected baseline. We conduct a comparative analysis of longitudinal car-following characteristics and frequency-domain features between these two groups. Furthermore, random forest, logistic regression, and support vector machine models are employed to diagnose whether HVs undergo systematic restructuring under AV-imposed following constraints. The results show that a random forest model achieves approximately 96% classification accuracy, indicating a highly separable structural difference between AVLVs and SLVs in the behavioral space. SHAP-based interpretability analysis reveals that, compared with SLVs, AVLVs exhibit more stable longitudinal following and lower instantaneous conflict risk, but also more frequent high-frequency, small-amplitude speed adjustments and more constrained speed autonomy. This coexistence of “stable following and high-frequency micro-adjustments” suggests that, under persistent longitudinal constraints imposed by AVs, HVs gain stability at the expense of local coordination efficiency. This study uncovers a previously overlooked mechanism of NECF triggered by mismatched human–machine driving styles in mixed traffic, providing new empirical evidence for mixed traffic flow modeling and the design of AV control strategies.
Liu et al. (Mon,) studied this question.