Grouptron: Dynamic multi-scale graph convolutional networks for group-aware dense crowd trajectory forecasting
Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive
scenes is a longstanding challenge. Recent advances in using data-driven approaches …
scenes is a longstanding challenge. Recent advances in using data-driven approaches …
SKGACN: social knowledge-guided graph attention convolutional network for human trajectory prediction
K Lv, L Yuan - IEEE Transactions on Instrumentation and …, 2023 - ieeexplore.ieee.org
Pedestrian trajectory prediction is crucial in driverless applications. To accurately predict the
high-quality trajectory of pedestrians, it is necessary to consider the reasonable social …
high-quality trajectory of pedestrians, it is necessary to consider the reasonable social …
Navigating robots in dynamic environment with deep reinforcement learning
In the fight against COVID-19, many robots replace human employees in various tasks that
involve a risk of infection. Among these tasks, the fundamental problem of navigating robots …
involve a risk of infection. Among these tasks, the fundamental problem of navigating robots …
Learning interaction-aware guidance policies for motion planning in dense traffic scenarios
B Brito, A Agarwal, J Alonso-Mora - arXiv preprint arXiv:2107.04538, 2021 - arxiv.org
Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
Deep imitation learning for autonomous navigation in dynamic pedestrian environments
Navigation through dynamic pedestrian environments in a socially compliant manner is still
a challenging task for autonomous vehicles. Classical methods usually lead to unnatural …
a challenging task for autonomous vehicles. Classical methods usually lead to unnatural …
Participants selection for from-scratch mobile crowdsensing via reinforcement learning
Participant selection is a major research challenge in Mobile Crowdsensing (MCS).
Previous approaches commonly assume that adequately long and fixed periods of …
Previous approaches commonly assume that adequately long and fixed periods of …
Dynamic window approach with human imitating collision avoidance
S Matsuzaki, S Aonuma… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
The autonomous navigation in the crowded environment is a challenging task due to the
sensor occlusion and the complex nature of the abstract social interactions. And yet, humans …
sensor occlusion and the complex nature of the abstract social interactions. And yet, humans …
Tra2tra: Trajectory-to-trajectory prediction with a global social spatial-temporal attentive neural network
Accurate trajectory prediction plays a key role in robot navigation. It is beneficial for planning
a collision-free and appropriate path for the autonomous robots, especially in crowded …
a collision-free and appropriate path for the autonomous robots, especially in crowded …
Stirnet: A spatial-temporal interaction-aware recursive network for human trajectory prediction
Pedestrian trajectory prediction is one of the important research topics in the field of
computer vision and a key technology of autonomous driving system. However, it's full of …
computer vision and a key technology of autonomous driving system. However, it's full of …
ST: Spatial-Temporal State Transformer for Crowd-Aware Autonomous Navigation
Empowering an intelligent agent with the ability of autonomous navigation in complex and
dynamic environments is an important and active research topic in embodied artificial …
dynamic environments is an important and active research topic in embodied artificial …