对比多个数据集的人类驾驶合规性，揭示安全行为差异与数据噪声问题。
Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations
- 基于多数据集轨迹分析人类驾驶规则遵守情况
- 发现部分数据集存在高噪声和异常驾驶行为
- 为自动驾驶系统训练提供数据质量优化参考
自动驾驶系统的发展迫切需要深入理解人类在多样化驾驶场景中的行为。分析人类驾驶数据对于构建模仿安全驾驶习惯的自主系统至关重要，有助于其在以人类为主的交通环境中无缝融入。本文对Argoverse 2、nuPlan、Lyft和DeepUrban等多个轨迹预测数据集中的交通与安全规则遵守情况进行了对比评估。通过使用现有安全与行为相关指标（如碰撞时间、限速遵守率、与其他交通参与者交互等），全面分析各数据集的优势与局限。研究重点关注数据样本分布，识别训练集与验证集中存在的噪声、异常值及不当驾驶行为。结果表明，部分数据集需采用稳健的数据过滤技术，因其存在高水平噪声和不良行为。
原文摘要 · Abstract (English)
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.
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