Processing, assessing, and enhancing the Waymo autonomous vehicle open dataset for driving behavior research

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.

Automated summary

This paper comprehensively processed and assessed Waymo Open Dataset, focusing on car following trajectories. The extracted trajectories have better quality than NGSIM dataset. By removing outliers and denoising, the trajectory data was further enhanced. The impact of outliers and noise on IDM calibration was also tested.