Processing, assessing, and enhancing the Waymo autonomous vehicle open dataset for driving behavior research
Processing, assessing, and enhancing the Waymo autonomous vehicle open dataset for driving behavior research
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DOI：
10.1016/j.trc.2021.103490
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发表时间：
2022-01-01
影响因子：
8.3
通讯作者：
Sun, Jian
中科院分区：
文献类型：
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作者：
Hu, Xiangwang;Zheng, Zuduo;Sun, Jian
最近发布的自动驾驶车辆(AV)轨迹数据集可能会促进面向自动驾驶车辆的交通流分析的研究进展。本文旨在对面向AV的开放数据集Waymo Open DataSet进行全面、系统的处理和评估，重点研究CAR跟踪的配对轨迹。首先，原始数据集被处理成用户友好的格式，其中包含与AV和周围对象的行为相关的所有重要信息。其次，从内部一致性、突变值和轨迹完整性方面对数据质量进行了评估。结果表明，提取的轨迹都是不完整的，但总体上比下一代仿真程序(NGSIM)的数据集具有更好的质量。第三，利用基于优化的孤立点去除方法和小波去噪方法对轨迹数据进行了进一步的增强。此外，我们还测试了数据异常值和噪声对IDM校准的影响，发现所需时间间隔T和最大加速度a的参数值存在显著差异。
Recently released Autonomous Vehicle (AV) trajectory datasets can potentially catalyze research progress on AV-oriented traffic flow analysis. This paper aims to comprehensively and systematically process and assess one of the AV-oriented open datasets, i.e., Waymo Open Dataset, with a focus on car following paired trajectories. First, the original dataset has been processed into a user-friendly format which contains all important information related to the behavior of AV and surrounding objects. Second, the data quality has been assessed in terms of internal consistency, jerk values and trajectory completeness. Results show that the extracted trajectories are all incomplete but generally they have better quality than that of Next Generation Simulation program (NGSIM) dataset. Third, the trajectory data has been further enhanced by using an optimization-based outlier removal method and a wavelet denoising method. Additionally, we have tested the impact of data outliers and noise on IDM calibration, and revealed significant differences in parameter values for desired time gap T and maximum acceleration a.