发表机构
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用真实世界数据集和频域框架，量化了量产自动驾驶车辆与人工驾驶车辆在不同场景下的运动学差异，发现差异具有场景依赖性，凸显了多场景评估的必要性。
AI 中文摘要
关于量产自动驾驶车辆（PAVs）与人工驾驶车辆（HVs）之间车辆运动学特性的差异，实证研究十分有限。最近的研究多依赖于基于仿真的模型，部分研究在受控实验中进一步考察了低级自适应巡航控制（ACC）系统。这些方法通常采用一些时域指标来表征有限驾驶条件下的PAV-HV差异。然而，当前配备高级自动驾驶系统的PAV通过数据驱动模型以黑箱方式生成驾驶行为。这些根本不同的行为生成机制可能在交通中产生独特的运动学特性。更重要的是，这些时域指标无法反映不同驾驶场景下与频率相关的交通动态。因此，本研究采用了包含四个PAV平台的真实世界PAV数据集，并开发了一个频域框架，以量化PAV与HV在不同驾驶场景（包括不同驾驶状态、光照、天气和车辆密度）下的运动学差异。该框架将运动学信号转换到频域并提取频谱特征，然后基于核密度估计和Wasserstein距离比较PAV与HV之间的这些特征。结果揭示了清晰的、依赖场景的PAV-HV频谱差异。具体而言，跟车行驶时速度相关差异始终小于巡航时，而雨天条件下加速度相关差异相比晴天条件始终增大。这些发现强调了多场景评估的必要性，并展示了频域分析在表征真实世界条件下PAV-HV运动学差异方面的价值。
英文摘要
Differences in vehicle kinematic characteristics between production autonomous vehicles (PAVs) and human-driven vehicles (HVs) have been limitedly investigated by empirical studies. Most recent studies rely on simulation-based models, while some further investigate low-level adaptive cruise control (ACC) systems in controlled experiments. These methods commonly adapt some time-domain metrics to characterize PAV-HV differences across limited driving conditions. However, current PAVs equipped with high-level autonomous driving systems generate driving behaviors in a black box using data-driven models. These fundamentally different mechanisms for generating behaviors may produce distinct kinematic characteristics in traffic. More importantly, these time-domain metrics cannot reflect frequency-related traffic dynamics across different driving scenarios. Thus, this study adapted a real-world PAV dataset with four PAV platforms and developed a frequency-domain framework to quantify kinematic differences between PAVs and HVs across diverse driving scenarios, including varying driving states, lighting, weather, and vehicle densities. The framework transforms kinematic signals into the frequency domain and extracts spectral features, and then compares these features between PAVs and HVs based on kernel density estimation and Wasserstein distance. The results reveal clear scenario-dependent PAV-HV spectral differences. Specifically, speed-related differences were consistently smaller during car-following than cruising, while rainy conditions consistently enlarged acceleration-related differences compared with clear conditions. These findings highlight the necessity of multi-scenario evaluations and demonstrate the value of frequency-domain analysis for characterizing PAV-HV kinematic differences under real-world conditions.
