The increasing interest in autonomous driving systems has highlighted the need for an in-depth analysis of human driving behavior in diverse scenarios. Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices and ensure seamless integration into human-dominated environments. This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets, including Argoverse 2, nuPlan, Lyft, and DeepUrban. By defining and leveraging existing safety and behavior-related metrics, such as time to collision, adherence to speed limits, and interactions with other traffic participants, we aim to provide a comprehensive understanding of each datasets strengths and limitations. Our analysis focuses on the distribution of data samples, identifying noise, outliers, and undesirable behaviors exhibited by human drivers in both the training and validation sets. The results underscore the need for applying robust filtering techniques to certain datasets due to high levels of noise and the presence of such undesirable behaviors.

翻译：自动驾驶系统日益增长的需求凸显了对多样化场景下人类驾驶行为进行深入分析的必要性。分析人类驾驶数据对于开发能够复现安全驾驶行为、并确保顺利融入人类主导环境的自动驾驶系统至关重要。本文通过多个轨迹预测数据集（包括Argoverse 2、nuPlan、Lyft和DeepUrban）对人类遵守交通与安全规则的情况进行了比较评估。通过定义并利用现有的安全与行为相关指标（如碰撞时间、限速遵守度、与其他交通参与者的交互等），我们旨在全面揭示各数据集的优势与局限性。我们的分析聚焦于数据样本的分布特征，识别训练集和验证集中人类驾驶员表现出的噪声、异常值及不良驾驶行为。研究结果表明，由于部分数据集存在较高噪声水平和不良行为，需要对其应用鲁棒的数据过滤技术。