Categorizing Merging and Diverging Strategies of Truck Drivers at Motorway Ramps and Weaving Sections using a Trajectory Dataset

Sharma, Salil; Snelder, M.; Tavasszy, Lóránt; Lint, Hans van

doi:10.1177/0361198120932568

Cited by 8 publications

(2 citation statements)

References 24 publications

(44 reference statements)

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“…Zhu et al, 2022), and the presence of heavy goods vehicles in merging (e.g. Dreger et al, 2020;Sharma et al, 2020). Future research could also focus on the development of feedback and cooperative systems that support the merging/lane change decision (e.g.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Now or never: Eye tracking and response times reveal the dynamics of highway merging decisions

Zgonnikov,

van Niekerk,

Eisma

et al. 2023

Preprint

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Merging onto a highway is a safety-critical task resulting in a large number of traffic accidents; fundamental research into merging behavior of human drivers can help reduce this toll. Two cognitive processes critical to merging, attention allocation and decision making, have been extensively studied in both natural human behavior in real traffic and simulated driving. However, the interaction between attention and decision making during merging remains poorly understood. To address this gap, here we investigated the relationship between kinematics of the merging scenario, gap acceptance decisions, response timing, and eye movements of drivers during merging. In a video-based experiment, our participants (N=24) repeatedly made gap acceptance decisions based on the information available in the front view and the side mirror while their response times and eye movements were recorded. We found that decisions to accept a gap were much faster than decisions to reject a gap: the dynamically decreasing gap demanded that participants accept gaps in a timely manner. Furthermore, merging decisions and their timing depended on the distance and time-to-arrival to the overtaking vehicle, but also on the time pressure imposed by the approaching end of the merging lane. Most importantly, the proportion of time participants spent looking at the rear-view mirror was associated with differences in the probability of accepting the gap, controlling for duration of the decision-making process. This effect was particularly pronounced in conditions with low time budgets, suggesting that appropriate glancing behavior can be critical for merging decisions under time pressure. Our results lay foundation for future efforts on cognitive modeling of the interplay between decision-making and attention processes during merging and can aid in understanding unsafe merging maneuvers related to improper attention distribution.

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Section: Limitations and Future Directionsmentioning

confidence: 99%

Now or never: Eye tracking and response times reveal the dynamics of highway merging decisions

Zgonnikov,

van Niekerk,

Eisma

et al. 2023

Preprint

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show abstract

“…In this paper, a trajectory dataset is used to develop the gap selection decision models for multiple vehicle classes [17]. This dataset is a freely available resource that has been used in research before to understand driving behavior [18,23]. The data comprise vehicle trajectories obtained through aerial imaging in the vicinity of 14 infrastructural bottlenecks located in the Netherlands, which include 3 on-ramps, 3 offramps, and 8 weaving sections (see Fig.…”

Section: A Trajectory Datamentioning

confidence: 99%

Unraveling Gap Selection Process During Discretionary Lane Changing by Vehicle Class

et al. 2022

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This paper studies and compares the gap selection process of multiple vehicle classes (passenger cars, delivery vans, and trucks) within their discretionary lane changing activities. Given a trajectory or a sequence of gap selection decisions, we aim to predict whether a vehicle will change or keep a lane. For this purpose, we use a large trajectory dataset, collected for the Netherlands, consisting of 3,647 trajectories of passenger car drivers, 1,080 trajectories of delivery van drivers, and 2,226 trajectories of truck drivers. We apply gated recurrent unit neural networks to separately model their gap selection processes. These three models can not only handle class imbalance but also capture long-term interdependencies. The models can predict gap selection of three vehicle classes with geometric mean accuracies of 84% or higher.To obtain insights into their gap selection processes, we apply a gradient-based technique to analyze what neural networks have learned. Our results suggest that there exist significant differences between vehicle classes in terms of the importance of historical information and features. Trucks seem to value a relatively longer period, recent 6 seconds, of driving experience to select gaps compared to passenger cars and delivery vans. In addition, the perception of road topology seems to be a significant factor for delivery vans and trucks, contrary to passenger cars which highly value their kinematic features and interactions with surrounding vehicles, to select gaps. These insights offer a novel contribution towards better understanding and modeling of the driving behavior of multiple vehicle classes.

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Modeling and analysis of mandatory lane-changing behavior considering heterogeneity in means and variances

Zhao

Tang

et al. 2023

Physica A: Statistical Mechanics and its Applications

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Categorizing Merging and Diverging Strategies of Truck Drivers at Motorway Ramps and Weaving Sections using a Trajectory Dataset

Cited by 8 publications

References 24 publications

Now or never: Eye tracking and response times reveal the dynamics of highway merging decisions

Now or never: Eye tracking and response times reveal the dynamics of highway merging decisions

Unraveling Gap Selection Process During Discretionary Lane Changing by Vehicle Class

Modeling and analysis of mandatory lane-changing behavior considering heterogeneity in means and variances

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