Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong Special Administrative Region.
School of Transportation Science and Engineering, Beihang University, China.
Accid Anal Prev. 2022 Jul;172:106689. doi: 10.1016/j.aap.2022.106689. Epub 2022 May 12.
As the market penetration rate of automated vehicles (AVs) increases, there will be a transition period when the traffic stream is composed of both AVs and human-driven vehicles (MVs) in the near future. However, the interactions between MVs and AVs, especially whether MVs will behave differently when following AVs compared to following MVs, have not been fully understood. Previous studies in this field mainly conducted traffic/numerical simulations or field experiments to investigate human drivers' behavior changes, but these approaches all have critical drawbacks such as simplified driving environments and limited sample sizes. To fill in the knowledge gap, this study uses the high-resolution (10 Hz) Waymo Open Dataset to reveal differences in car-following behaviors between MV-following-AV and MV-following-MV cases. Driving volatility measures, time headways and time-to-collision (TTC) are adopted to quantify and compare MV-following-AV and MV-following-MV interactions. The principal component analysis (PCA) is applied on the high-dimensional feature space, followed by the hierarchical clustering on the dimension-reduced feature set to categorize MV driving styles when following AVs. The comparison results indicate that MV-following-AV events have lower driving volatility in terms of velocity and acceleration/deceleration, smaller time headways and higher TTC values. Furthermore, the clustering results reveal that human drivers when following AVs exhibit four different car-following styles: high-velocity-non-aggressive, high-velocity-aggressive, low-velocity-non-aggressive, and low-velocity-aggressive. These findings highlight the vital importance of taking into account the heterogeneity of MV-following-AV behaviors when designing mixed traffic control algorithms and can be beneficial for AV fleet operators to improve their algorithms.
随着自动驾驶汽车（AV）的市场渗透率不断提高，在不久的将来，交通流将由 AV 和人类驾驶车辆（MV）共同组成。然而，MV 和 AV 之间的相互作用，尤其是 MV 在跟随 AV 和跟随 MV 时的行为是否会有所不同，尚未得到充分理解。该领域的先前研究主要通过交通/数值模拟或现场实验来研究人类驾驶员行为的变化，但这些方法都存在简化驾驶环境和有限样本量等关键缺陷。为了填补这一知识空白，本研究利用高分辨率（10 Hz）的 Waymo 开放数据集，揭示了 MV 跟随 AV 和 MV 跟随 MV 情况下跟车行为的差异。采用驾驶波动指标、时距和碰撞时间（TTC）来量化和比较 MV 跟随 AV 和 MV 跟随 MV 的相互作用。在高维特征空间上应用主成分分析（PCA），然后在降维特征集上进行层次聚类，以对跟随 AV 时的 MV 驾驶风格进行分类。比较结果表明，MV 跟随 AV 事件的速度和加速度/减速度波动较小，时距较小，TTC 值较高。此外，聚类结果表明，跟随 AV 的驾驶员表现出四种不同的跟车风格：高速非激进型、高速激进型、低速非激进型和低速激进型。这些发现强调了在设计混合交通控制算法时考虑 MV 跟随 AV 行为异质性的重要性，并且对于 AV 车队运营商改进他们的算法也具有重要意义。
