An improved GAN with transformers for pedestrian trajectory prediction models
Z Lv, X Huang, W Cao - International Journal of Intelligent …, 2022 - Wiley Online Library
Predicting the future trajectories of multiple pedestrians in certain scenes is critical for
autonomous moving platforms (like, self‐driving cars and social robots). In this paper, we …
autonomous moving platforms (like, self‐driving cars and social robots). In this paper, we …
Learning crowd-aware robot navigation from challenging environments via distributed deep reinforcement learning
S Matsuzaki, Y Hasegawa - 2022 International conference on …, 2022 - ieeexplore.ieee.org
This paper presents a deep reinforcement learning (DRL) sframework for safe and efficient
navigation in crowded environments. Here, the robot learns cooperative behavior using a …
navigation in crowded environments. Here, the robot learns cooperative behavior using a …
Trajectory design in UAV-aided mobile crowdsensing: A deep reinforcement learning approach
X Tao, AS Hafid - ICC 2021-IEEE International Conference on …, 2021 - ieeexplore.ieee.org
Mobile crowdsensing (MCS) is a method of data collection by recruiting mobile devices to
accomplish various sensing tasks. The mobility and intelligence of mobile devices enable an …
accomplish various sensing tasks. The mobility and intelligence of mobile devices enable an …
TPPO: a novel trajectory predictor with pseudo oracle
Forecasting pedestrian trajectories in dynamic scenes remains a critical problem in various
applications, such as autonomous driving and socially aware robots. Such forecasting is …
applications, such as autonomous driving and socially aware robots. Such forecasting is …
CSR: cascade conditional variational auto encoder with socially-aware regression for pedestrian trajectory prediction
Pedestrian trajectory prediction is a key technology in many real applications such as video
surveillance, social robot navigation, and autonomous driving, and significant progress has …
surveillance, social robot navigation, and autonomous driving, and significant progress has …
Disentangled multi-relational graph convolutional network for pedestrian trajectory prediction
Pedestrian trajectory prediction is one of the important tasks required for autonomous
navigation and social robots in human environments. Previous studies focused on …
navigation and social robots in human environments. Previous studies focused on …
Robot navigation in crowds via deep reinforcement learning with modeling of obstacle uni-action
X Lu, H Woo, A Faragasso, A Yamashita… - Advanced …, 2023 - Taylor & Francis
Mobile robots operating in public environments require the ability to navigate among
humans and obstacles in a socially compliant and safe manner. Previous work has shown …
humans and obstacles in a socially compliant and safe manner. Previous work has shown …
Pedestrian trajectory prediction via spatial interaction transformer network
T Su, Y Meng, Y Xu - 2021 IEEE Intelligent Vehicles …, 2021 - ieeexplore.ieee.org
As a core technology of the autonomous driving system, pedestrian trajectory prediction can
significantly enhance the function of active vehicle safety and reduce road traffic injuries. In …
significantly enhance the function of active vehicle safety and reduce road traffic injuries. In …
Pedestrian trajectory prediction based on deep convolutional LSTM network
Pedestrian trajectory prediction is vital for transportation systems. Generally we can divide
pedestrian behavior modeling into two categories, ie, knowledge-driven and data-driven …
pedestrian behavior modeling into two categories, ie, knowledge-driven and data-driven …
CF-LSTM: Cascaded feature-based long short-term networks for predicting pedestrian trajectory
Pedestrian trajectory prediction is an important but difficult task in self-driving or autonomous
mobile robot field because there are complex unpredictable human-human interactions in …
mobile robot field because there are complex unpredictable human-human interactions in …