Loughborough University
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Investigating autonomous vehicle discretionary lane-changing execution behaviour: Similarities, differences, and insights from Waymo dataset

journal contribution
posted on 2024-06-20, 15:47 authored by Yasir AliYasir Ali, Anshuman Sharma, Danjue Chen
Recently released autonomous vehicle datasets like Waymo can provide rich information (and unprecedented opportunities) to investigate lane-changing behaviour of autonomous vehicles, requiring data from multiple drivers and lanes with different objectives. As such, the study investigates the discretionary lane-changing execution behaviour of autonomous vehicles and compares its behaviour with human-driven vehicles from Waymo and Next Generation Simulation (NGSIM) datasets. Several behavioural factors are statistically analysed and compared, whereas the discretionary lane-changing execution time (or duration) is modelled by a random parameters hazard-based duration modelling approach, which accounts for unobserved heterogeneity. Descriptive analyses suggest that autonomous vehicles maintain larger lead and lag gaps, longer discretionary lane-changing execution time, and lower acceleration variation than human-driven vehicles. The random parameters duration model reveals heterogeneity in discretionary lane-changing execution behaviour, which is higher in human-driven vehicles but decreases significantly for autonomous vehicles. Whilst contradictory to a general hypothesis in the literature that autonomous vehicles will eliminate heterogeneity, our finding indicates that heterogeneous behaviour also exists in autonomous vehicles (although to a lesser extent than in human-driven vehicles), which can be contextual to prevailing traffic conditions. Overall, autonomous vehicles show safer discretionary lane-changing behaviour compared to human-driven vehicles.

Funding

NSF CMMI Awards # 2401555 and # 2401476

History

School

  • Architecture, Building and Civil Engineering

Published in

Analytic Methods in Accident Research

Volume

42

Issue

2024

Publisher

Elsevier

Version

  • AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in Analytic Methods in Accident Research published by Elsevier. The final publication is available at https://doi.org/10.1016/j.amar.2024.100332. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

2024-03-29

Publication date

2024-04-06

Copyright date

2024

eISSN

2213-6657

Language

  • en

Depositor

Dr Yasir Ali. Deposit date: 14 June 2024

Article number

100332