scholarly journals Social graph convolutional LSTM for pedestrian trajectory prediction

2021 ◽  
Vol 15 (3) ◽  
pp. 396-405
Author(s):  
Yutao Zhou ◽  
Huayi Wu ◽  
Hongquan Cheng ◽  
Kunlun Qi ◽  
Kai Hu ◽  
...  
Keyword(s):  
Trajectory Prediction ◽  
Social Graph ◽  
Convolutional Lstm

Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network

2020 ◽  
pp. 1-18
Author(s):  
Xiao Song ◽  
Kai Chen ◽  
Xu Li ◽  
Jinghan Sun ◽  
Baocun Hou ◽  
...  
Keyword(s):  
Trajectory Prediction ◽  
Lstm Network ◽  
Convolutional Lstm

Forward-Backward Convolutional LSTM for Acoustic Modeling

2017 ◽  
Author(s):  
Shigeki Karita ◽  
Atsunori Ogawa ◽  
Marc Delcroix ◽  
Tomohiro Nakatani
Keyword(s):  
Acoustic Modeling ◽  
Convolutional Lstm

scholarly journals Experimental Validation on Intersection Turning Trajectory Prediction Method for Advanced Driver Assistance System Based on Triclothoidal Curve

2021 ◽  
Vol 11 (13) ◽  
pp. 5900
Author(s):  
Yohei Fujinami ◽  
Pongsathorn Raksincharoensak ◽  
Shunsaku Arita ◽  
Rei Kato
Keyword(s):  
Traffic Accidents ◽  
Computational Cost ◽  
Prediction Method ◽  
Smooth Curve ◽  
Assistance System ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Trajectory Prediction ◽  
Left Turn ◽  
Left Hand

Advanced driver assistance systems (ADAS) for crash avoidance, when making a right-turn in left-hand traffic or left-turn in right-hand traffic, are expected to further reduce the number of traffic accidents caused by automobiles. Accurate future trajectory prediction of an ego vehicle for risk prediction is important to activate the assistance system correctly. Our objectives are to propose a trajectory prediction method for ADAS for safe intersection turnings and to evaluate the effectiveness of the proposed prediction method. Our proposed curve generation method is capable of generating a smooth curve without discontinuities in the curvature. By incorporating the curve generation method into the vehicle trajectory prediction, the proposed method could simulate the actual driving path of human drivers at a low computational cost. The curve would be required to define positions, angles, and curvatures at its initial and terminal points. Driving experiments conducted at real city traffic intersections proved that the proposed method could predict the trajectory with a high degree of accuracy for various shapes and sizes of the intersections. This paper also describes a method to determine the terminal conditions of the curve generation method from intersection features. We set a hypothesis where the conditions can be defined individually from intersection geometry. From the hypothesis, a formula to determine the parameter was derived empirically from the driving experiments. Public road driving experiments indicated that the parameters for the trajectory prediction could be appropriately estimated by the obtained empirical formula.

scholarly journals Probabilistic Bounds on Vehicle Trajectory Prediction Using Scenario Approach

2020 ◽  
Vol 53 (2) ◽  
pp. 2385-2390
Author(s):  
Xun Shen ◽  
Xingguo Zhang ◽  
Pongsathorn Raksincharoensak
Keyword(s):  
Trajectory Prediction ◽  
Vehicle Trajectory ◽  
Vehicle Trajectory Prediction ◽  
Scenario Approach

A prediction model of vessel trajectory based on generative adversarial network

2021 ◽  
pp. 1-11
Author(s):  
Senjie Wang ◽  
Zhengwei He
Keyword(s):  
Safety Management ◽  
Route Planning ◽  
Local Area ◽  
Trajectory Data ◽  
Trajectory Prediction ◽  
Generative Adversarial Network ◽  
Ship Traffic ◽  
Adversarial Network ◽  
Historical Trajectory ◽  
Vessel Motion

Abstract Trajectory prediction is an important support for analysing the vessel motion behaviour, judging the vessel traffic risk and collision avoidance route planning of intelligent ships. To improve the accuracy of trajectory prediction in complex situations, a Generative Adversarial Network with Attention Module and Interaction Module (GAN-AI) is proposed to predict the trajectories of multiple vessels. Firstly, GAN-AI can infer all vessels’ future trajectories simultaneously when in the same local area. Secondly, GAN-AI is based on adversarial architecture and trained by competition for better convergence. Thirdly, an interactive module is designed to extract the group motion features of the multiple vessels, to achieve better performance at the ship encounter situations. GAN-AI has been tested on the historical trajectory data of Zhoushan port in China; the experimental results show that the GAN-AI model improves the prediction accuracy by 20%, 24% and 72% compared with sequence to sequence (seq2seq), plain GAN, and the Kalman model. It is of great significance to improve the safety management level of the vessel traffic service system and judge the degree of ship traffic risk.

scholarly journals A Two-Layer Switching based Trajectory Prediction Method

2021 ◽  
Author(s):  
Stefan Reisinger ◽  
Daniel Adelberger ◽  
Luigi del Re
Keyword(s):  
Prediction Method ◽  
Trajectory Prediction
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