Analyzing Rear-End Collision Risk Relevant to Autonomous Vehicles by Using a Humanlike Brake Model
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 7
Abstract
Rear-end collisions between autonomous vehicles (AVs) and human-driven vehicles (HVs) represent critical scenarios in road networks. Few studies have focused on the scenarios where a HV hits an AV from behind (called a HV-AV collision). This paper aims to investigate the occurrence of HV-AV collisions in the stop-in-lane (SiL) scenario where a HV follows an AV. A humanlike brake control (HLBC) model is firstly proposed to simulate the driver brake control. The HLBC model considers human driving intention, vision-based expectancy, and certain inherent characteristics of human driving to achieve dynamic humanlike braking. Additionally, the joint distribution of off-road-glance and time-headway is originally introduced to simulate the glance distraction of drivers during their dynamic vehicle control. Sequentially, we apply the HLBC model to the SiL scenario to investigate how the HV-AV collision probability changes with respect to various dynamic driving parameters. The results of the case study provide a thorough understanding of the dynamic driving conditions that lead to HV-AV collisions and pave the way for identifying practical countermeasures to improve the road safety involving AVs.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
Acknowledgments
This study was supported by Heilongjiang Provincial Natural Science Foundation of China No. LH2023E055, and the Fundamental Research Funds for the Central Universities (Science and Technology Leading Talent Team Project) No. 2022JBXT003.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 7 • July 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Aug 30, 2023
Accepted: Feb 22, 2024
Published online: May 3, 2024
Published in print: Jul 1, 2024
Discussion open until: Oct 3, 2024
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