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Analysis of Time-to-Lane-Change-Initiation Using Realistic Driving Data

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Title: Analysis of Time-to-Lane-Change-Initiation Using Realistic Driving Data
Authors: Jokhio, Sarang, Olleja, Pierluigi, 1995, Bärgman, Jonas, 1972, Yan, Fei, Baumann, Martin
Source: IEEE Transactions on Intelligent Transportation Systems. 25(5):4620-4633
Subject Terms: Roads, Trajectory, Regulation, Europe, autonomous vehicles, Analytical models, Vehicles, Lane change, Radar, realistic driving data, time-to-lane-change-initiation, mixed effect Cox model
Description: Lane changing is a complex, yet extremely common driving manoeuvre. Studying lane changes can provide insight into how long drivers wait after activating their turn signal before changing lanes -a time that we call time-to-lane-change-initiation (TTLCI). TTLCI can offer valuable insights into driver behaviour prior to changing lanes. However, a better understanding of TTLCI, particularly in real-world settings, is lacking. To address this knowledge gap, we investigated TTLCI using driving data collected on public roads in Gothenburg, Sweden. We used the Kaplan-Meier (K-M) method and the mixed-effect Cox Proportional Hazard (CPH) model (statistical techniques from survival analysis) to comprehensively analyze TTLCI and identify factors that significantly influence it. The results of the K-M method indicate that most lane changes were initiated within two seconds of activating the turn signal. The mixed-effect CPH model showed that the speed of the lane-changing vehicle, the type and direction of the lane change, the presence of lead and lag vehicles, and the lag gap were all significant factors. These findings provide new insights into pre-lane-change behaviour and pave the way for future studies, in part by improving current lane change models. Moreover, the findings have implications for future regulations concerning turn-signal usage by human drivers. Additionally, our results can contribute to the development of algorithms for autonomous vehicles by improving their ability to detect imminent lane changes by surrounding vehicles.
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  Data: Analysis of Time-to-Lane-Change-Initiation Using Realistic Driving Data
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  Data: <searchLink fieldCode="AR" term="%22Jokhio%2C+Sarang%22">Jokhio, Sarang</searchLink><br /><searchLink fieldCode="AR" term="%22Olleja%2C+Pierluigi%22">Olleja, Pierluigi</searchLink>, 1995<br /><searchLink fieldCode="AR" term="%22Bärgman%2C+Jonas%22">Bärgman, Jonas</searchLink>, 1972<br /><searchLink fieldCode="AR" term="%22Yan%2C+Fei%22">Yan, Fei</searchLink><br /><searchLink fieldCode="AR" term="%22Baumann%2C+Martin%22">Baumann, Martin</searchLink>
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  Data: <i>IEEE Transactions on Intelligent Transportation Systems</i>. 25(5):4620-4633
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  Label: Description
  Group: Ab
  Data: Lane changing is a complex, yet extremely common driving manoeuvre. Studying lane changes can provide insight into how long drivers wait after activating their turn signal before changing lanes -a time that we call time-to-lane-change-initiation (TTLCI). TTLCI can offer valuable insights into driver behaviour prior to changing lanes. However, a better understanding of TTLCI, particularly in real-world settings, is lacking. To address this knowledge gap, we investigated TTLCI using driving data collected on public roads in Gothenburg, Sweden. We used the Kaplan-Meier (K-M) method and the mixed-effect Cox Proportional Hazard (CPH) model (statistical techniques from survival analysis) to comprehensively analyze TTLCI and identify factors that significantly influence it. The results of the K-M method indicate that most lane changes were initiated within two seconds of activating the turn signal. The mixed-effect CPH model showed that the speed of the lane-changing vehicle, the type and direction of the lane change, the presence of lead and lag vehicles, and the lag gap were all significant factors. These findings provide new insights into pre-lane-change behaviour and pave the way for future studies, in part by improving current lane change models. Moreover, the findings have implications for future regulations concerning turn-signal usage by human drivers. Additionally, our results can contribute to the development of algorithms for autonomous vehicles by improving their ability to detect imminent lane changes by surrounding vehicles.
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        Value: 10.1109/TITS.2023.3329690
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      – SubjectFull: Roads
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      – SubjectFull: Trajectory
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      – SubjectFull: Regulation
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      – SubjectFull: Vehicles
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      – SubjectFull: Lane change
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      – SubjectFull: Radar
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      – SubjectFull: realistic driving data
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      – SubjectFull: time-to-lane-change-initiation
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      – TitleFull: Analysis of Time-to-Lane-Change-Initiation Using Realistic Driving Data
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