Review of pedestrian trajectory prediction methods: Comparing deep learning and knowledge-based approaches

R Korbmacher, A Tordeux - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
In crowd scenarios, predicting trajectories of pedestrians is a complex and challenging task
depending on many external factors. The topology of the scene and the interactions …

Learning quadrotor dynamics for precise, safe, and agile flight control

A Saviolo, G Loianno - Annual Reviews in Control, 2023 - Elsevier
This article reviews the state-of-the-art modeling and control techniques for aerial robots
such as quadrotor systems and presents several future research directions in this area. The …

Chatgpt for robotics: Design principles and model abilities

SH Vemprala, R Bonatti, A Bucker, A Kapoor - IEEE Access, 2024 - ieeexplore.ieee.org
This paper presents an experimental study regarding the use of OpenAI's ChatGPT for
robotics applications. We outline a strategy that combines design principles for prompt …

A survey on trajectory-prediction methods for autonomous driving

Y Huang, J Du, Z Yang, Z Zhou… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …

Hivt: Hierarchical vector transformer for multi-agent motion prediction

Z Zhou, L Ye, J Wang, K Wu… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Accurately predicting the future motions of surrounding traffic agents is critical for the safety
of autonomous vehicles. Recently, vectorized approaches have dominated the motion …

Leapfrog diffusion model for stochastic trajectory prediction

W Mao, C Xu, Q Zhu, S Chen… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a
sophisticated multi-modal distribution of future trajectories. Emerging diffusion models have …

Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting

Y Yuan, X Weng, Y Ou… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Predicting accurate future trajectories of multiple agents is essential for autonomous systems
but is challenging due to the complex interaction between agents and the uncertainty in …

Multimodal motion prediction with stacked transformers

Y Liu, J Zhang, L Fang, Q Jiang… - Proceedings of the …, 2021 - openaccess.thecvf.com
Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety
of autonomous driving. Recent motion prediction approaches attempt to achieve such …

Eqmotion: Equivariant multi-agent motion prediction with invariant interaction reasoning

C Xu, RT Tan, Y Tan, S Chen… - Proceedings of the …, 2023 - openaccess.thecvf.com
Learning to predict agent motions with relationship reasoning is important for many
applications. In motion prediction tasks, maintaining motion equivariance under Euclidean …

Scene transformer: A unified architecture for predicting multiple agent trajectories

J Ngiam, B Caine, V Vasudevan, Z Zhang… - arXiv preprint arXiv …, 2021 - arxiv.org
Predicting the motion of multiple agents is necessary for planning in dynamic environments.
This task is challenging for autonomous driving since agents (eg vehicles and pedestrians) …