A deep learning framework for modelling left-turning vehicle behaviour considering diagonal-crossing motorcycle conflicts at mixed-flow intersections

Yao, Ruoyu; Zeng, Weiliang; Chen, Yi-Hao; He, Zhaoshui

doi:10.1016/j.trc.2021.103415

Cited by 18 publications

(3 citation statements)

References 52 publications

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“…Also, the data for training and evaluation were both from simulations. In [28], a long short-term memory (LSTM)-based network was employed to encode vehicle historical motion features, the graph attention network (GAT) and a synthesized network were integrated to model vehicle-vehicle interaction and vehicle-motorcycle interaction.…”

Section: Literature Reviewmentioning

confidence: 99%

Autonomous vehicles’ intended cooperative motion planning for unprotected turning at intersections

Zhou

Zhang

et al. 2022

IET Intelligent Trans Sys

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Turning behaviour is one of the most challenging driving manoeuvres that take place at intersections. Autonomous vehicles (AVs) are often overly conservative in these scenarios as they compromised to others’ behaviour to realize behavioural consistency. This paper proposes an intended cooperative motion planning (ICMP) that can actively predict the intentions of interacting participants. This helps achieve socially compliant cooperation and enables each vehicle to converge upon a set of consistent behaviours. The ICMP framework divides the integrated driving process into two related modules: prediction and planning. These modules are integrated using the mechanism of intended cooperation, which first introduces the criteria for a cooperative response and then maps them in the motion planning space. The prediction module applies a Gaussian mixture model to learn human drivers’ behaviour to determine conflict points, which helps to narrow down the solution spaces. In the planning module, paths are represented by quartic Bézier curves and speed is modelled as a polynomial function. Optimizations can then be used that maximize the efficiency and smoothness for path planning, and comfort and efficiency for speed planning. The test results show that ICMP‐based AVs had consistent interactions with other vehicles. Moreover, when compared with a potential field‐based method, the ICMP‐based method had better performance in terms of safety, comfort and efficiency, especially when interacting with multiple oncoming vehicles.

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Section: Literature Reviewmentioning

confidence: 99%

Autonomous vehicles’ intended cooperative motion planning for unprotected turning at intersections

Zhou

Zhang

et al. 2022

IET Intelligent Trans Sys

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“…Some of them studied the behavior of motorcycle riders approaching or passing through signalized intersections. A few studies investigated the maneuvering behavior in queues [1,2] and the crossing behavior [29,30] of motorcycle riders at signalized urban intersections. These studies explored various behaviors of motorcycle riders riding along urban road networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Factors Influencing Stopping Locations of Motorcycle Riders on Signalized Urban Intersection Approaches

Promraksa

Satiennam

et al. 2022

Sustainability

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In developing countries, motorcycle riders normally attempt to stop at their desired locations during queue formation on signalized intersection approaches. Under mixed-traffic conditions, motorcycle positioning in a queue affects the operational and safety performance of the intersection. This study aimed to identify factors influencing motorcycle riders’ stopping locations at signalized urban intersections. This study applied Unmanned Aerial Vehicles (UAVs) to observe the stopping behavior of 1413 motorcycle riders on 24 approaches from 10 signalized intersections in Thailand (N = 1413). Multinomial logistic regression analysis was used to determine the relationship between the stopping locations of motorcycle riders and rider- and motorcycle-related variables and traffic- and environmental-related variables. The statistical analyses presented a Cox and Snell R2 and Nagelkerke R2 of 0.466 and 0.499, respectively, indicating that the model accounted for almost 50% of the variation among the five stopping locations of motorcycle riders. The results showed that, under mixed-traffic conditions in Thailand with left-hand traffic, motorcycle riders intending to turn right, the morning peak period, the presence of shadows, motorcycle riders not wearing helmets, the presence of a larger vehicle in the queue, and the density of desired stopping locations significantly influenced the motorcyclists’ choice of stopping locations on signalized intersection approaches. Practical policy-related recommendations drawn from the findings are provided to improve motorcyclists’ safety on signalized intersection approaches.

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“…Additionally, in contrast to trajectory prediction models, which are commonly compared to each other on accepted benchmarks (such as on the ETH dataset [10], [11], [12] when predicting pedestrian crowds), an equivalent benchmark does not exist for binary prediction models [46]. Instead, those models are mostly trained and tested on datasets exclusive to the respective work and are-if at all-only compared against a small number of other selected models [32], [33], [38], [39], [40], [47], [48], [49], [50], [51].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Behavior Prediction Models in Gap Acceptance Scenarios

Schumann

Kober

Zgonnikov

2023

IEEE Trans. Intell. Veh.

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A deep learning framework for modelling left-turning vehicle behaviour considering diagonal-crossing motorcycle conflicts at mixed-flow intersections

Cited by 18 publications

References 52 publications

Autonomous vehicles’ intended cooperative motion planning for unprotected turning at intersections

Autonomous vehicles’ intended cooperative motion planning for unprotected turning at intersections

Factors Influencing Stopping Locations of Motorcycle Riders on Signalized Urban Intersection Approaches

Benchmarking Behavior Prediction Models in Gap Acceptance Scenarios

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