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<p>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.</p>

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Non-explicit Coordination Failure Induced by Autonomous Vehicles: Evidence from Waymo

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月15日

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  • Tingyu Liu,
  • Lanfang Zhang,
  • Zhenyu Zhao,
  • Tianyuan Han

摘要

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.