Sarkar, A, Hickman, JS, McDonald, AD et al. (3 more authors) (2021) Steering or braking avoidance response in SHRP2 rear-end crashes and near-crashes: A decision tree approach. Accident Analysis and Prevention, 154. 106055. ISSN 0001-4575
Abstract
Objective
The paper presents a systematic analysis of drivers’ crash avoidance response during crashes and near-crashes and developed a machine learning-based predictive model that can determine driver maneuver using pre-incident driver behavior and driving context.
Methods
We analyzed 286 naturalistic rear-end crashes and near-crashes from the SHRP2 naturalistic driving study. All the events were manually reduced using face video (face and forward) and kinematic responses. In this paper, we developed new reduction variables that enhanced the understanding of drivers’ gaze behavior and roadway attention behavior during these events. These features reflected how the event criticality, measured using time to collision, related to drivers’ pre-incident behavior (secondary behavior, gaze behavior), and drivers’ perception of the event (physical reaction and maneuver). The imperative understanding of such relations was validated using a random forest- (RF) based classifier, which efficiently predicted if a driver was going to brake or change the lane as an avoidance maneuver.
Results
The RF presented in this paper effectively explored the nonlinear patterns in the data and was highly accurate (∼96 %) in its prediction. A further analysis of the RF model showed that six features played a pivotal role in the decision logic. These included the drivers’ last glance duration before the event, last glance eccentricity, duration of ‘eyes on road’ immediately before the event, the time instance and criticality when the driver perceives the threat as well as acknowledge the threat, and possibility of an escape path in the adjacent lane. Using partial dependency plots, we also showed how different thresholds of these feature variables determined the drivers’ maneuver intention.
Conclusions
In this paper we analyzed driving context, drivers’ behavior, event criticality, and drivers’ response in a unified structure to predict their avoidance response. To the best of our knowledge, this is the first such effort where large-scale naturalistic data (crashes and near crashes) was analyzed for prediction of drivers’ maneuver and determined key behavioral and contextual factors that contribute to this avoidance maneuver.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Ltd. All rights reserved. This is an author produced version of an article published in Accident Analysis and Prevention. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Avoidance response; Gaze eccentricity; SHRP 2 naturalistic driving; Random forest; Driver maneuver; Rear-end events |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/S005056/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Mar 2021 16:31 |
Last Modified: | 07 Mar 2022 01:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.aap.2021.106055 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:171780 |
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Filename: Steer or Brake_Sarkar et al _AAP_Feb2021.pdf
Licence: CC-BY-NC-ND 4.0