Vehicle Trajectory Prediction at Intersections using Interaction based Generative Adversarial Networks

Roy, Debaditya; Ishizaka, Tetsuhiro; Mohan, C. Krishna; Fukuda, Atsushi

doi:10.1109/itsc.2019.8916927

Cited by 77 publications

(20 citation statements)

References 15 publications

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“…Due to the lack of prior work on the same task, we extend several state-of-the-art trajectory forecasting methods shown below as baselines. First, person detection results obtained in Section IV-B1 were tracked over time with the SORT tracker [42], which is used widely in many practical forecasting tasks, e.g., [22], [23], and interpolated linearly to create training and testing trajectory samples. Predicted future trajectories were mapped onto the input space and smoothed by a Gaussian filter to form future crowd density maps as done for the ground-truth maps in Section IV-B2.…”

Section: E Baseline Methodsmentioning

confidence: 99%

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Crowd Density Forecasting by Modeling Patch-Based Dynamics

Minoura

Yonetani

Nishimura

et al. 2021

IEEE Robot. Autom. Lett.

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Section: E Baseline Methodsmentioning

confidence: 99%

“…There is also some work tackling a similar forecasting task in robotics domains such as mobile robot navigation [3]- [6] and intelligent vehicles [7]- [9], [22], [23]. Recent work proposed a method that took auto-camera videos and LiDAR data as input to forecast vehicle trajectories [24], [25].…”

Section: B Trajectory Forecasting In Robotics Domainmentioning

confidence: 99%

Crowd Density Forecasting by Modeling Patch-Based Dynamics

Minoura

Yonetani

Nishimura

et al. 2021

IEEE Robot. Autom. Lett.

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“…There is a wide range of possibilities to build GANs architectures. Roy et al [116] employed a GAN for trajectory prediction of vehicles using aerial images. The training procedure consists of generating trajectories similar to the groundtruth (real trajectories), until the generator produces trajectories indistinguishable from the real ones.…”

Section: G Generative Adversarial Networkmentioning

confidence: 99%

A Review on Intention-aware and Interaction-aware Trajectory Prediction for Autonomous Vehicles

Gomes¹,

Wolf²

2022

Preprint

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This paper presents a literature review on Intention-aware and Interaction-aware Trajectory Prediction for Autonomous Vehicle, which covers primary studies since 2008. The research questions try to understand how intention/interaction improves prediction, and what are the techniques and technologies employed in trajectory prediction.<div>Moreover, this paper is under review in the IEEE Transactions on Intelligent Transportation Systems.</div>

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“…However, the dependency of the participants is much wider than in the classic microscopic redicts one trajectories a couple of manoeuvre [42]. A more sophisticated solution is proposed in [43].…”

Section: Mesoscopic Analysismentioning

confidence: 99%

Traffic Swarm Behaviour: Machine Learning and Game Theory in Behaviour Analysis

Hollósi¹,

Lukovszki²,

Bancsics³

et al. 2021

Infocommunications journal

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High density traffic on highways and city streets consists of endless interactions among participants. These interactions and the corresponding behaviours have great impact not only on throughput of traffic but also on safety, comfort and economy. Because of this, there is a great interest in deeper understanding of these interactions and concluding the impacts on traffic participants. This paper explores and maps the world of traffic behaviour analysis, especially researches focusing on groups of vehicles called traffic swarm, while presents the state-of-the-art methods and algorithms. The conclusion of this paper states that there are special areas of traffic behaviour analysis which have great research potential in the near future to describe traffic behaviour in more detail than present methods.

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Vehicle Trajectory Prediction at Intersections using Interaction based Generative Adversarial Networks

Cited by 77 publications

References 15 publications

Crowd Density Forecasting by Modeling Patch-Based Dynamics

Crowd Density Forecasting by Modeling Patch-Based Dynamics

A Review on Intention-aware and Interaction-aware Trajectory Prediction for Autonomous Vehicles

Traffic Swarm Behaviour: Machine Learning and Game Theory in Behaviour Analysis

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