Characterizing Behavioral Differences of Autonomous Vehicles and Human-Driven Vehicles at Signalized Intersections Based on Waymo Open Dataset

Yiyun Wang*, Haneen Farah, Rongjie Yu, Shuhan Qiu, Bart van Arem

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

31 Downloads (Pure)

Abstract

Autonomous vehicles (AVs) are being introduced to the traffic system with the promise of improving current traffic status. However, the empirical data also indicate contrary effects with estimated higher crash rate and change of crash patterns. Therefore, it is necessary to investigate the driving behavior of AVs and human-driven vehicles (HDVs) in real mixed traffic. Current studies have analyzed the driving behavior of AVs and HDVs, as well as behavioral adaptations of drivers of HDVs based on empirical data. While they play an important role in traffic systems, signalized intersections have not been studied sufficiently in this context. Therefore, this study aims to utilize the Waymo open dataset to characterize and quantify the behavioral differences of AVs and HDVs at signalized intersections. Five parameters of driving behavior related to signalized intersections were characterized according to five critical maneuver phases, which were identified by wavelet transform and threshold-based method. Statistically significant differences in driving behavior between AVs and HDVs were found, from three categorized situations: vehicle approaching the red light/queue, vehicle responding to the green light (as the first vehicle), and vehicle responding to its preceding vehicle (in the queue). Further, behavioral adaptations of HDV drivers were revealed in that they tended to keep closer to the stopped AVs in a queue and to react more strongly to AV start-up maneuvers when the traffic light turns to green.

Original languageEnglish
Pages (from-to)324-337
Number of pages14
JournalTransportation Research Record
Volume2677
Issue number11
DOIs
Publication statusPublished - 2023

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • automated/autonomous vehicles
  • operations
  • traffic flow
  • traffic flow theory and characteristics

Fingerprint

Dive into the research topics of 'Characterizing Behavioral Differences of Autonomous Vehicles and Human-Driven Vehicles at Signalized Intersections Based on Waymo Open Dataset'. Together they form a unique fingerprint.

Cite this